JP2021172264A - Vehicle driving support method and driving support device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a vehicle traveling support method and a traveling support device capable of avoiding a risk.
SOLUTION: It is determined whether or not it is necessary to change the lane of the own vehicle in order to travel along the route, and if it is determined that the lane change is necessary, at least one of the actual risk potential and the predicted risk potential. Using either one, calculate the lane change risk potential due to the risk associated with the driving motion of the lane change, and add the lane change risk potential to the predicted risk potential of the own lane in which the own vehicle is traveling to obtain the risk potential of the own lane. Use the risk potential of your lane to set the position of the lane change.
[Selection diagram] Fig. 3

Description

The present invention relates to a vehicle traveling support method and a traveling support device.

As this type of driving support device, based on the trajectory, position, and lane information of the own vehicle and traffic participants, the traffic participants around the own vehicle are classified according to their attributes and conditions, and the traffic participation around the own vehicle is performed. Based on the classification result of the person and the actual risk learned in advance for each classification, the actual risk according to the classification is applied to each traffic participant around the own vehicle to generate an actual risk map, and a plurality of own vehicles are generated. Among the states corresponding to the positions on the route candidates of, it is determined as the optimum action to transition or stop to the state in which a large amount of reward is obtained by the reward function using the actual risk map, and the action determined in this way. A vehicle control device that controls its own vehicle according to the above is known (Patent Document 1).

JP-A-2019-106049

However, in the above-mentioned conventional technology, since the optimum behavior is determined by using the actual risk map after detecting the traffic participants such as surrounding automobiles and pedestrians, it is possible to support the driving of the detected object, but the encounter is expected. It is not possible to provide driving support that corresponds to the risks involved. That is, the above-mentioned conventional technique has a problem that driving support that avoids risks cannot be performed.

An object to be solved by the present invention is to provide a vehicle traveling support method and a traveling support device that can avoid risks in advance.

The present invention obtains the risk potential of an object detected by a vehicle, associates the obtained risk potential with the encounter position where the object is encountered, accumulates the risk potential at the encounter position as the actual risk potential, and accumulates the accumulated manifestation. Using the risk potential, the predicted risk potential of the object predicted to be encountered at the encounter position, which is lower than the actual risk potential obtained when the object is detected, is obtained. Then, when traveling along the route to the destination and traveling again at the encounter position, the traveling of the vehicle is autonomously controlled by using the predicted risk potential.

In the autonomous control, the present invention determines whether or not it is necessary to change the lane of the own vehicle in order to travel along the route, and if it is determined that the lane change is necessary, the actual risk potential and the predicted risk. Calculate the lane change risk potential due to the risk associated with the driving operation of the lane change using at least one of the potential, and add the lane change risk potential to the predicted risk potential of the own lane in which the own vehicle travels to obtain the risk of the own lane. Find the potential and use the risk potential of your lane to set the position of the lane change.

According to the present invention, since the risk potential can be predicted before reaching the encounter position where the detected object is encountered, it is possible to provide running support that can avoid the risk even before the detected object is detected.

It is a block diagram which shows the running support system including the running support method and the running support device of the vehicle of this invention. It is a flowchart which shows the information processing procedure in the driving support system of FIG. It is a block diagram which shows one Embodiment of the route calculation part of FIG. It is a top view which shows an example of the traveling path from the present position to the destination set by the route planning part of the traveling support device of FIG. It is a top view which shows an example of the traffic condition at a certain date and time in the traveling route of FIG. It is a figure which shows the accumulation example of the information of the peripheral object obtained as a result of traveling on the traveling path of FIG. 4 a plurality of times, and is stored in the storage part of FIG. It is a figure which shows an example of the risk potential and the encounter probability generated by the prediction risk map generation part of FIG. 3 using the accumulation information of the peripheral object of FIG. FIG. 5 is a plan view showing an example of a predicted running operation of another vehicle that avoids a risk due to a predicted risk potential in the traffic condition shown in FIG. It is a top view which shows an example of the prediction risk map generated by the prediction risk map generation part of FIG. 3 about the traveling route of FIG. FIG. 5 is a plan view showing the difficulty level of changing the lane of the own vehicle at each position of the road in the traffic condition shown in FIG. It is a top view which shows the risk map which integrated the lane change risk potential generated by the lane change risk potential generation part of FIG. 3 with the prediction risk map of FIG. It is a plan view which shows an example of the final travel path determined by the action determination part of FIG. 3 with reference to the risk map of FIG. 11 with respect to the travel path of FIG. 4 (No. 1). FIG. 2 is a plan view showing an example of a final travel route determined by the action determination unit of FIG. 3 with reference to the risk map of FIG. 11 with respect to the travel route of FIG. 4 (No. 2). FIG. 3 is a plan view showing an example of a final travel route determined by the action determination unit of FIG. 3 with reference to the risk map of FIG. 11 with respect to the travel route of FIG. 4 (No. 3). It is a top view which shows the risk map which integrated the actual risk map generated by the actual risk map generation part of FIG. 3 with the predicted risk map of FIG. It is a flowchart (No. 1) which shows an example of the information processing procedure in the route calculation part of FIG. It is a flowchart (No. 2) which shows an example of the information processing procedure in the route calculation part of FIG. It is a flowchart (No. 3) which shows an example of the information processing procedure in the route calculation part of FIG. FIG. 5 is a plan view showing an example of a final travel route determined by the action determination unit of FIG. 3 with reference to the risk map of FIG. 9 with respect to the travel route of FIG.

Hereinafter, embodiments of the present invention will be described with reference to the drawings.

The vehicle traveling support method and the vehicle traveling support device according to the present invention can be applied to autonomous driving control that autonomously executes vehicle speed control and vehicle steering control, and when the driver manually drives. It can also be applied to a navigation system that presents an appropriate driving route and assists the driver in manual driving. When applied to autonomous driving control of a vehicle, it can be applied not only to autonomous control of both speed control and steering control, but also to autonomous control of one of speed control and steering control and manual control of the other. Hereinafter, an example in which the vehicle traveling support method and the vehicle traveling support device according to the present invention are applied to a vehicle having an autonomous driving control function will be described.

The description of the following embodiments is based on the premise that the vehicle travels on the left side in a country having a left-side traffic regulation. In countries that have right-hand traffic regulations, vehicles travel on the right-hand side, so the right and left sides of the explanation below shall be read symmetrically.

FIG. 1 is a block diagram showing a configuration of the traveling support system 1000. The travel support system 1000 of the present embodiment includes a travel support device 100 and a vehicle control device 200. The travel support device 100 of the present embodiment includes a communication device 111, the vehicle control device 200 also includes a communication device 211, and the travel support device 100 and the vehicle control device 200 exchange information with each other by wired communication or wireless communication. conduct.

More specifically, the driving support system 1000 of the present embodiment includes a detection device 1, a navigation device 2, a map information 3 stored in a readable recording medium, a vehicle information detection device 4, and an environment recognition device 5. The object recognition device 6, the travel support device 100, and the vehicle control device 200 are provided. The detection device 1, the navigation device 2, the map information 3 stored in a readable recording medium, the own vehicle information detection device 4, the environment recognition device 5, the object recognition device 6, and the traveling support device 100. As shown in FIG. 1, each device is connected by a CAN (Controller Area Network) or other in-vehicle LAN in order to exchange information with each other.

The detection device 1 of the present embodiment detects information on the driving environment including the presence of obstacles located around the own vehicle, such as the entire surroundings of the front, side, and rear of the own vehicle, and other conditions around the own vehicle. do. The detection device 1 of the present embodiment includes an image pickup device for recognizing environmental information around the own vehicle, for example, a camera including an image pickup element such as a CCD, an ultrasonic camera, an infrared camera, and the like. The imaging device of the present embodiment is installed in the own vehicle, images the surroundings of the own vehicle, and acquires image data including the target vehicle existing around the own vehicle.

The detection device 1 of the present embodiment includes a distance measuring device, and the distance measuring device calculates the relative distance and the relative speed between the own vehicle and the object. The information of the object detected by the distance measuring device is output to the processor 10. As the distance measuring device, a laser radar, a millimeter wave radar or the like (LRF or the like), a LiDAR (light detection and ranging) unit, an ultrasonic radar or the like known at the time of filing can be used.

As the detection device 1 of the present embodiment, one or more image pickup devices and a distance measuring device can be adopted. The detection device 1 of the present embodiment complements the lacking information in the detection information by integrating or synthesizing the detection information of the image pickup device and the information of a plurality of different devices such as the detection information of the distance measuring device. It has a sensor fusion function that uses environmental information around the vehicle. This sensor fusion function may be incorporated into the environment recognition device 5, the object recognition device 6, or other controller or logic.

Objects detected by the detection device 1 are lane boundaries, center lines, road signs, medians, guard rails, curbs, sidewalks of highways, road signs, traffic lights, crosswalks, construction sites, accident sites, and traffic. Includes restrictions and lane closures. Objects detected by the detection device 1 include automobiles (other vehicles) other than the own vehicle, motorcycles, bicycles, and pedestrians. The object detected by the detection device 1 includes an obstacle. Obstacles are objects that may affect the running of your vehicle. The detection device 1 detects at least information about obstacles. The object detected by the detection device 1 is detected by the self-position information such as GPS, which is the position where the own vehicle travels, and the relative position (distance and direction) between the own vehicle and the object. be able to. Further, the object detected by the detection device 1 is the position of the object based on the map information, the self-position information which is the position where the own vehicle travels by odometry, and the relative position (distance and direction) between the own vehicle and the object. Information can be detected by associating it with map information.

The navigation device 2 of the present embodiment refers to the map information 3 and calculates a travel lane / travel route from the current position detected by the own vehicle information detection device 4 to the destination. The traveling lane or traveling route is a linear shape in which the road, direction (up / down) and lane on which the own vehicle travels are identified. The travel route includes information on the travel lane. Hereinafter, the traveling lane may be abbreviated as a lane.

The map information 3 of the present embodiment is stored in a readable state in a recording medium provided in the travel support device 100, the in-vehicle device, or the server device, and is used for route generation and / or operation control. The map information 3 of the present embodiment includes road information, facility information, and attribute information thereof. Road information and road attribute information include road width, radius of curvature, shoulder structure, road traffic regulations (speed limit, lane changeability), road confluences, branch points, lane increase / decrease positions, etc. Contains information. The map information 3 of the present embodiment is so-called high-definition map information, and according to the high-definition map information, the movement locus for each lane can be grasped. High-definition map information includes two-dimensional position information and / or three-dimensional position information at each map coordinate, road / lane boundary information at each map coordinate, road attribute information, lane up / down information, lane identification information, and connection destination. Includes lane information.

Further, the map information 3 of the present embodiment includes information on the track boundary indicating the boundary between the track on which the own vehicle travels and the rest. The runway on which the own vehicle travels is a road on which the own vehicle travels, and the form of the runway is not particularly limited. The track boundary exists on each of the left and right sides with respect to the traveling direction of the own vehicle. The form of the track boundary is not particularly limited, and examples thereof include road markings and road structures. Examples of the track boundary of the road marking include a lane boundary line and a center line. Further, the runway boundary of the road structure includes, for example, a median strip, a guardrail, a curb, a tunnel, or a side wall of a highway. Note that the track boundary is set in advance in the map information 3 for a point (for example, inside an intersection) where the track boundary cannot be clearly specified. The preset track boundaries are fictitious track boundaries, not road markings or road structures that actually exist.

The own vehicle information detection device 4 of the present embodiment acquires detection information regarding the state of the own vehicle. The state of the own vehicle includes the current position, speed, acceleration, attitude, and vehicle performance of the own vehicle. These may be acquired from the vehicle control device 200 of the own vehicle, or may be acquired from each device of the own vehicle. The own vehicle information detection device 4 of the present embodiment acquires the current position of the own vehicle based on the information acquired from the GPS (Global Positioning System) unit, the gyro sensor, and the odometry of the own vehicle. Further, the own vehicle information detection device 4 of the present embodiment acquires the speed and acceleration of the own vehicle from the vehicle speed sensor of the own vehicle. Further, the own vehicle information detection device 4 of the present embodiment acquires the attitude data of the own vehicle from the inertial measurement unit (IMU) of the own vehicle.

The environment recognition device 5 of the present embodiment includes position information acquired by the detection device 1, image information around the own vehicle, object recognition information obtained from distance measurement information, and information on the environment constructed based on map information. Recognize. The environment recognition device 5 of the present embodiment generates environmental information around the own vehicle by integrating a plurality of pieces of information. The object recognition device 6 of the present embodiment also uses the map information 3 and predicts the recognition and movement of the object around the own vehicle by using the image information and the distance measurement information around the own vehicle acquired by the detection device 1.

The vehicle control device 200 of the present embodiment is an in-vehicle computer such as an electronic control unit (ECU), and electronically controls a drive mechanism 210 that controls the operation of the vehicle. The vehicle control device 200 controls the drive device, the braking device, and the steering device included in the drive mechanism 210 to drive the own vehicle according to the target vehicle speed and the target travel route. A control command based on the operation plan of the own vehicle is input to the vehicle control device 200 from the travel support device 100. The target vehicle speed, target travel route, and operation plan of the own vehicle will be described later.

The drive mechanism 210 of the present embodiment includes an electric motor and / or an internal combustion engine as a traveling drive source, a power transmission device including a drive shaft and an automatic transmission for transmitting the output from these traveling drive sources to the drive wheels, and power transmission. A driving device for controlling the device, a braking device for braking the wheels, a steering device for controlling the total steering wheel according to the steering angle of the steering wheel (so-called steering wheel), and the like are included. A control signal corresponding to the target vehicle speed is input to the vehicle control device 200 from the travel support device 100. The vehicle control device 200 generates each control signal of the drive mechanism 210 based on the control signal input from the travel support device 100, and executes operation control including acceleration / deceleration of the vehicle. By transmitting control information to the drive device of the drive mechanism 210, the speed control of the vehicle can be autonomously controlled.

Further, the vehicle control device 200 of the present embodiment uses any one or more of the lane information stored in the map information 3, the information recognized by the environment recognition device 5, and the information acquired by the object recognition device 6. Therefore, the steering device of the drive mechanism 210 is controlled so that the own vehicle travels while maintaining a predetermined lateral position (position in the left-right direction of the vehicle) with respect to the target travel path. The steering device includes a steering actuator, and the steering actuator includes a motor and the like attached to a column shaft of the steering. A control signal corresponding to the target travel route is input to the vehicle control device 200 from the travel support device 100. The steering device of the drive mechanism 210 executes steering control of the vehicle based on a control signal input from the vehicle control device 200. By transmitting control information to the steering device of the drive mechanism 210, the steering control of the vehicle can be autonomously controlled.

The travel support device 100 of the present embodiment executes control to support the travel of the own vehicle by controlling the operation of the own vehicle. As shown in FIG. 1, the travel support device 100 of the present embodiment includes a processor 10. The processor 10, which is a control device, serves as a traveling support device 100 by executing a ROM 12 which is a ROM (Read Only Memory) in which a program for executing operation control of the own vehicle is stored and a program stored in the ROM 12. It is a computer including a CPU 11 which is a CPU (Central Processing Unit) as a functioning operation circuit and a RAM 13 which is a RAM (Random Access Memory) which functions as an accessible storage device. The processor 10 of the present embodiment controls various functions by the cooperation of the software for realizing the above functions and the above-mentioned hardware. The processor 10 includes a communication device 111 and an output device 110, and issues various output or input commands, information reading permission or information provision commands to the vehicle control device 200, the navigation device 2, the map information 3, and the own vehicle information detection. Output to the device 4, the environment recognition device 5, and the object recognition device 6. The processor 10 exchanges information with the detection device 1, the navigation device 2, the map information 3, the own vehicle information detection device 4, the environment recognition device 5, the object recognition device 6, and the vehicle control device 200.

The processor 10 of the present embodiment includes a destination setting unit 120, a route planning unit 130, an operation planning unit 140, a travelable area calculation unit 150, a route calculation unit 160, and a driving behavior control unit 170. Each controls its own function. The processor 10 of the present embodiment includes the destination setting unit 120, the route planning unit 130, the operation planning unit 140, the travelable area calculation unit 150, the route calculation unit 160, and the driving behavior control unit 170, respectively. It is composed of software for realizing or executing each process and cooperation with the above-mentioned hardware.

The control procedure by the processor 10 of this embodiment will be described with reference to FIG. FIG. 2 is a flowchart showing an information processing procedure of the driving support system according to the present embodiment. An outline of the autonomous driving control process executed by the driving support device 100 will be described with reference to FIG.

First, in step S1 of FIG. 2, the processor 10 executes a process of acquiring the current position of the own vehicle based on the detection result of the own vehicle information detection device 4 by the destination setting unit 120, and in the subsequent step S2. Executes the process of setting the destination of the own vehicle. The destination may be input by the user or predicted by another device. In the following step S3, the processor 10 acquires various detection information including the map information 3 by the route planning unit 130. In the following step S4, the processor 10 sets the travel lane (or travel route) for the destination set by the destination setting unit 120 by the route planning unit 130. The processor 10 sets the traveling lane by the route planning unit 130 using the information obtained from the environment recognition device 5 and the object recognition device 6 in addition to the map information 3 and the self-position information. The processor 10 sets the road on which the own vehicle travels by the route planning unit 130, but sets the lane in which the own vehicle travels on the road, not limited to the road.

In the following step S5, the processor 10 executes a process of planning the driving behavior of the own vehicle at each point on the route by the driving planning unit 140. The driving plan defines driving behavior such as progress (GO) and stop (No-GO) at each point. For example, when turning right at an intersection, it is determined whether or not to stop at the position of the stop line, and the progress of a vehicle in the oncoming lane is determined.

In the following step S6, in order to execute the driving action planned in step S5, the processor 10 is subjected to the environment recognition device 5 and the object recognition device 6 in addition to the map information 3 and the self-position information by the travelable area calculation unit 150. Using the information obtained from the above, a process of calculating a travelable area (also referred to as a travelable area) around the own vehicle is executed. The travelable area is not limited to the lane in which the own vehicle travels, and may be a lane adjacent to the lane in which the own vehicle travels (also referred to as an adjacent lane). Further, the travelable area may be any area as long as the vehicle can travel, and may be a region other than the area recognized as a lane on the road.

In the following step S7, the processor 10 executes a process of generating a target travel route on which the own vehicle travels by the route calculation unit 160. In addition, the processor 10 calculates the target vehicle speed and the profile of the target vehicle speed when traveling along the target travel route by the driving behavior control unit 170. The processor 10 may calculate the target deceleration and the target acceleration with respect to the current vehicle speed, and their profiles, in place of or in combination with the target vehicle speed. The calculated target vehicle speed is fed back to the target travel route generation process, and the target travel route is generated so as to suppress changes in the behavior of the vehicle and movements (behavior) that the occupants of the vehicle feel uncomfortable. May be good. The generated target traveling route may be fed back to the target vehicle speed calculation process to calculate the target vehicle speed so as to suppress changes in the behavior of the vehicle and movements (behaviors) that make the occupants of the vehicle feel uncomfortable.

In step S8, the processor 10 executes a process of formulating an operation plan for driving the generated target travel route to the own vehicle. Further, the processor 10 executes a process of formulating an operation plan for traveling the own vehicle at the calculated target vehicle speed. Then, in step S9, the output device 110 of the processor 10 outputs a control command and a control command value based on the operation plan to the vehicle control device 200 via the communication device 111, and operates the drive mechanism 210 which is various actuators.

The vehicle control device 200 inputs a vertical force and a lateral force for controlling the traveling position of the own vehicle based on the command value from the processor 10. According to these inputs, the behavior of the vehicle body and the behavior of the wheels are controlled so that the own vehicle autonomously travels following the target travel route. Based on these controls, at least one of the drive actuator and the braking actuator of the vehicle body drive mechanism 210 operates autonomously as necessary, and autonomous driving control to the destination is executed. NS. Of course, the drive mechanism 210 can also be operated according to the command value based on the manual operation.

By the way, the travel support device 100 of the present embodiment uses the function of the route calculation unit 160 when generating the target travel route in step S7 of FIG. The route calculation unit 160 of the present embodiment has, for example, the configuration shown in the block diagram of FIG. The route calculation unit 160 of the present embodiment executes a process of generating a target travel route on which the own vehicle travels, and in order to obtain the predicted risk potential and the lane change risk potential for that purpose, the locus acquisition unit 1601 of peripheral objects and Peripheral object classification unit 1602, peripheral object information storage unit 1603, storage unit 1604, prediction risk map generation unit 1605, actual risk map learning unit 1614, actual risk map generation unit 1615, and setting route reading unit. It includes 1610, a lane change necessity determination unit 1611, a lane change risk determination unit 1612, a lane change risk potential generation unit 1613, a risk map integration unit 1616, and an action determination unit 1617. Further, the predicted risk map generation unit 1605 includes a risk potential calculation unit 1606, an encounter probability calculation unit 1607, a prediction risk potential generation unit 1608, and an interrupted risk potential generation unit 1609. The interrupted risk potential generation unit 1609 can be omitted if necessary. Each of these parts 1601 to 1617 can be realized by a software program installed in the ROM 12 of the traveling support device 100. It should be noted that each of these parts 1601 to 1617 is merely classified for convenience in explaining the functions exhibited by the execution of the software program, and therefore does not determine the scope of rights.

The route calculation unit 160 shown in FIGS. 1 and 3 describes the following embodiments as being provided on the vehicle, but the route calculation unit 160 according to the present invention, particularly the peripheral object classification unit 1602 and the periphery of FIG. Object information storage unit 1603, storage unit 1604, predicted risk map generation unit 1605, risk potential calculation unit 1606, encounter probability calculation unit 1607, predicted risk potential generation unit 1608, interrupted risk potential generation unit 1609, actual risk map learning unit 1614, actual risk map generation unit 1615, setting route reading unit 1610, lane change necessity determination unit 1611, lane change risk determination unit 1612, lane change risk potential generation unit 1613, risk map integration unit 1616, and action determination unit 1617 , It is not always necessary to be provided on the vehicle side, and some or all of these may be provided on the server or the like. When a part or all of the parts constituting the route calculation unit 160 are provided in a vehicle other than the vehicle such as a server, and the remaining parts are provided in the vehicle, information can be transmitted and received between the vehicle and the server via the Internet or the like. It can be done in real time via a telecommunication network. When some or all of the actual risk potential, the predicted risk potential, the interrupt risk potential, and the lane change risk potential are calculated by the server, when autonomously controlling the running of the vehicle using at least one of these. In addition, the actual risk potential, the predicted risk potential, the interrupt risk potential, and the lane change risk potential corresponding to the self-position information can be acquired from the server.

It is also possible to provide the information storage unit 1603 and the storage unit 1604 of peripheral objects in the server, and store information about the objects detected by a plurality of vehicles in the information storage unit 1603 and the storage unit 1604 of these peripheral objects. In this case, the vehicle that detected the object and the vehicle that uses the risk potential information do not necessarily have to match.

Further, the predicted risk potential generated by the predicted risk potential generation unit 1608 of the predicted risk map generation unit 1605, the interrupted risk potential generated by the interrupted risk potential generation unit 1609, and the lane change generated by the lane change risk potential generation unit 1613. The timing of calculating the risk potential may be the timing of calculating in advance by the server and accumulating the risk potential in the server, or instead of the timing of traveling at the encounter position. Further, one of the actual risk map generated by the actual risk map generation unit 1615, the predicted risk map generated by the predicted risk map generation unit 1605, and the integrated risk map generated by the risk map integration unit 1616 is generated by the server. And others may be generated by the vehicle. The predicted risk potential, interruption risk potential, and lane change risk potential may be calculated from accumulated data, or calculated based on information such as construction work and traffic congestion that can be obtained from infrastructure such as road traffic systems. May be good.

The locus acquisition unit 1601 of the peripheral object acquires the trajectories of the traffic participants around the own vehicle. Traffic participants include automobiles, pedestrians, bicycles, motorcycles, and other objects (obstacles such as construction sections). In addition, automobiles include preceding vehicles, parked vehicles, rearmost vehicles, outflow vehicles (vehicles that branch from the current lane to another lane), merging vehicles (vehicles that merge from another lane into the current lane), and vehicles that become obstacles. , Other vehicles are included. In addition, pedestrians include children, the elderly, and other pedestrians according to age, and pedestrians who are stopped, walking, or running. Bicycles include children, the elderly, and other age-appropriate bicycles, as well as bicycles that are stopped, running at low speeds, or running at high speeds. In addition, motorcycles include preceding motorcycles, stopped motorcycles, last motorcycles, outflow motorcycles (motorcycles that branch from the current lane to another lane), merged motorcycles (motorcycles that merge from another lane to the current lane), and obstacles. Includes motorcycles and other motorcycles.

The locus acquisition unit 1601 of a peripheral object detects a traffic participant or other object by using the own vehicle as a probe car while the own vehicle is traveling in an arbitrary place and using an image pickup device, a distance measuring device or other detection device 1. And the information on the position, speed, and direction of the traffic participant is sent to the classification unit 1602 of the surrounding object together with the time stamp. The peripheral object classification unit 1602 uses the above-mentioned traffic participant and other object classification criteria for each information of the position, speed, direction, and time of the traffic participant and other objects read from the peripheral object trajectory acquisition unit 1601. After classifying, it is sent to the information storage unit 1603 of surrounding objects and the actual risk map learning unit 1614. The position, speed, direction, and time information of traffic participants and other objects sent to the information storage unit 1603 of surrounding objects are the predicted risk potential, interruption risk potential, and lane change risk for subsequent driving support requests. It is used to generate potential. On the other hand, each information of the position, speed, direction, and time of the traffic participant and other objects sent to the actual risk map learning unit 1614 is used to generate the actual risk map for the current driving support. NS.

In addition to classifying traffic participants and other objects as described above, the peripheral object classification unit 1602 classifies objects including traffic participants acquired by the peripheral object trajectory acquisition unit 1601 in particular in the present embodiment. , An object that blocks a lane for a long time, an object that temporarily blocks a lane, an object that obstructs a traffic flow, or an object that partially obstructs a traffic flow. For example, if the detected object is a parked vehicle or a construction section, it is classified as an object that blocks the lane for a long time, and the detected object is a vehicle waiting for a right or left turn or a stopped bus. If the movement is currently stopped but the traffic flow disappears over time, it is classified as an object that temporarily blocks the lane. In addition, if the detected object disturbs the traffic flow even if the movement is not stopped, such as a merging vehicle or a congested vehicle peculiar to the lane, it is classified as an object that obstructs the traffic flow, and the detected object walks in the lane. If it is an object such as a pedestrian, a bicycle, or a two-wheeled vehicle that may be able to continue traveling by avoiding the vehicle in the lateral direction, it is classified as an object that partially obstructs the traffic flow.

For the detected object, the risk potential is set in advance for each of these categories, and the risk potential value for each category is used by the risk potential calculation unit 1606 described later. The risk potential here means an index of the high risk of approaching the own vehicle to an obstacle (risk feeling index), and the larger the value of the risk potential, the higher the risk of approaching the own vehicle to the obstacle. become. Relative values are used because they are indicators of risk. For example, the magnitude relationship of the risk potential for pedestrians among traffic participants is preset as follows: child pedestrian> elderly pedestrian> other pedestrian. Children and old people are the same vulnerable people as other pedestrians, but children are more active than old people, so sudden jumps to vehicles are expected. Therefore, the risk potential of children's pedestrians is set to the highest value. In this way, the risk potential is preset for all the traffic participants and other objects from the viewpoint of the high risk of approaching the own vehicle.

In particular, in the present embodiment, high risk potential is set in the order of an object that blocks a lane for a long time, an object that temporarily blocks a lane, an object that obstructs a traffic flow, and an object that partially obstructs a traffic flow. That is, from the viewpoint of an object that obstructs a traffic flow in a lane, it is classified into four types: an object that obstructs a lane for a long time, an object that temporarily obstructs a lane, an object that obstructs a traffic flow, and an object that partially obstructs a traffic flow. However, the magnitude relationship of the risk potential is defined as an object that blocks the lane for a long time> an object that temporarily blocks the lane> an object that obstructs the traffic flow> an object that partially obstructs the traffic flow.

The peripheral object information storage unit 1603 stores information on the positions, speeds, directions, and times of traffic participants and other objects classified by the peripheral object classification unit 1602 in the storage unit 1604. That is, while a plurality of vehicles including the own vehicle are traveling in an arbitrary place, the vehicle becomes a probe car, and the locus acquisition unit 1601 of the peripheral objects, the classification unit 1602 of the peripheral objects, the information storage unit 1603 of the peripheral objects, and the peripheral objects By repeating the processing by the storage unit 1604, the risk potentials of the traffic participants and other objects are associated with the position information of the position where each object is detected, and are sequentially accumulated in the storage unit 1604.

In addition to the position information of the position where the object is detected, the attribute information such as the date and time when the object is detected and / or the weather may be associated and stored in the storage unit 1604. In this case, you can associate attributes of the date when the object was detected, such as month, day of the week, public holidays, beginning / end of the month, or time attributes such as morning / afternoon / midnight, work time / leaving time, meal. You may associate attributes such as time zone.

When associating the attributes of the weather, the weather information may be acquired via a communication network such as the Internet, but the raindrop sensor included in the detection device 1 may be used to determine whether or not it is raining, or the own vehicle information detection device 4 may be used. By detecting the operating status of the wiper, it may be determined whether or not it is raining.

While a plurality of vehicles including the own vehicle are traveling in an arbitrary place, the vehicle becomes a probe car, and the locus acquisition unit 1601 of the peripheral object, the classification unit 1602 of the peripheral object, the information storage unit 1603 of the peripheral object, and the storage of the peripheral object in FIG. An example will be described in which information on peripheral objects is stored in the storage unit 1604 of FIG. 3 by repeating the processing by the unit 1604. FIG. 4 is a plan view showing an example of a traveling route from the current position P1 to the destination Px set by the route planning unit 130 of the traveling support device 100 of the present embodiment. For example, FIG. 4 is a plan view of the commuting route of the own vehicle V1. It shall be a part. FIG. 5 is a plan view showing an example of traffic conditions at a certain date and time on the travel route of FIG.

Here, in the driving scene shown in FIG. 4, it is assumed that the road is on the left side. Further, S1 to S4 in FIG. 4 represent traffic lights, and at the intersection C for left-hand traffic in FIG. 4, a vehicle traveling on the left side of the lane extending in the vertical direction of the drawing extends to the traffic light S1 in the vertical direction of the drawing. A vehicle traveling on the right side of the lane is on the traffic light S2, a vehicle traveling on the upper side of the lane extending in the left-right direction of the drawing is on the traffic light S3, and a vehicle traveling on the lower side of the lane extending in the left-right direction of the drawing is on the traffic light S3. It is assumed that the vehicle travels according to the traffic light S4. Further, in the traveling scene shown in FIG. 4, it is assumed that the green light on the left side of the traffic lights S1 and S2 is lit, and the red light on the right side of the traffic lights S3 and S4 is lit. The settings for the traffic lights S1 to S4 are the same in the driving scenes shown in FIGS. 5, 8 to 13, and 16.

The commuting route of the own vehicle V1 shown in FIG. 4 is changed from the central lane of the road D1 which is the current position P1 of the own vehicle V1 to the left lane before the intersection C as shown by the traveling route R1. As shown by R2, it is a traveling route that turns left at intersection C, enters the left lane of road D2, and goes straight. It is assumed that the own vehicle V1 travels on the commuting routes R1 and R2 shown in FIG. 4 (hereinafter, collectively referred to as a traveling route R) every day, and the traffic conditions at a certain date and time are as shown in FIG. do. The vehicles shown in FIG. 5 are all other vehicles. In the left lane of the road D1, there are other stopped vehicles V2a and V2b. In front of the other vehicles V2a and V2b, there is another vehicle V3a that is turning left at a low speed to enter the frontage road D3, and behind the other vehicle V3a is another vehicle waiting for the other vehicle V3a to turn left. There are V3b and V3c. Further, in the left lane of the road D1, there is a traffic jam of six other vehicles V4a to V4f waiting for a left turn at the intersection C. Further, in the right lane and the right turn dedicated lane of the road D1, there is congestion of nine other vehicles V5a to V5i waiting for a right turn.

In such a situation, assuming that the own vehicle V1 travels along the travel route R shown in FIG. 4 at a certain date and time (from 6:00 to 7:00 on April 1, 2020), the road D1 (first By detecting other vehicles V2a and V2b (risk potential is risk A) stopped in the left lane (lane 1) of the road section (the road section is 0001), lane 1 of the road section 0001 at this date and time It is remembered that there was an actual risk potential of risk A. In addition, another vehicle V3a trying to enter the side road D3 from the left lane of the road D1, and other vehicles V3b and V3c waiting for a left turn of the other vehicle V3a behind the other vehicle V3a (risk potential is defined as risk B). ), And 6 other vehicles V4a to V4f (risk potential is risk B) waiting for a left turn at intersection C lined up in the left lane of road D1 on this date and time (April 1, 2020 6). It is memorized that there was an actual risk potential of risk B in lane 1 of the road section 0001 (hour to 7:00). In addition, five other vehicles V5a to V5i (risk potential is risk B) waiting for a right turn along the right lane (lane 3) and right turn lane (lane 4) of road D1. By detecting, it is memorized that there was an actual risk potential of risk B in lanes 3 and 4 of the road section 0001 on this date and time (April 1, 2020, 6:00 to 7:00). On the other hand, since no object is detected in the central lane (referred to as lane 2) of the other road section 0001, the risk potential related to the object detection is stored as 0.

FIG. 6 is a diagram showing an example of accumulating information on peripheral objects obtained as a result of traveling along the traveling path of FIG. 4 a plurality of times and stored in the storage unit 1604 of FIG. In the columns of risks A to D in the figure, the number "1" indicates that the corresponding risk is "yes", and the number "0" indicates that the corresponding risk is "none". As described above, by traveling on the commuting route R under the traffic conditions shown in FIG. 5 from 6:00 to 7:00 on April 1, 2020, the date of April 1, 2020 from 6:00 to 7:00 in FIG. 6 In the row column, it is remembered that lane 1 of road section 0001 had the actual risk potential of risk A and risk B, and that lanes 3 and 4 of road section 0001 had the actual risk potential of risk B. It is remembered that the risks of risks A to D were not detected in the road sections and lanes other than these.

Similarly, in the row column of the date from 6:00 to 7:00 on April 2, 2020 in FIG. 6, it is remembered that there was an actual risk potential of risk B in lane 1 of the road section 0001, and roads other than these. It is remembered that no risks of risks A to D were detected in the sections and lanes. Furthermore, in the line column of the date from 6:00 to 7:00 on April 3, 2020 in FIG. 8, there was an actual risk potential of risk A and risk B in lane 1 of road section 0001, and the lane of road section 0001. It is memorized that there was an actual risk potential of risk B in 3 and 4, and it is memorized that the risks of risks A to D were not detected in the road sections and lanes other than these.

Returning to FIG. 3, the route calculation unit 160 of the present embodiment aims from the current position P1 of the own vehicle V1 when the own vehicle V1 inputs the destination Px and starts running from now on or when making a running plan. For the entire area of the travel paths R1 and R2 leading to the ground Px, the actual risk potential for each travel position (also the encounter position with the detected object; the same applies hereinafter) stored in the storage unit 1604 in advance and the encounter with the object. From the probability, the predicted risk potential and the interrupted risk potential predicted for each traveling position are obtained, and the traveling route of the vehicle is set based on the predicted risk potential and the interrupted risk potential. In the following description, the risk potential for each traveling position and the method of obtaining the probability of encountering an object will be described for the range of the traveling path R shown in FIG. 6, but other ranges can be obtained by the same method.

First, how to obtain the predicted risk potential will be described. The risk potential calculation unit 1606 extracts the actual risk potential of each road section of the travel route from the current position P1 to the destination Px from the information stored in the storage unit 1604. In parallel with this, the encounter probability calculation unit 1607 obtains the encounter probability of each road section of the traveling route from the current position P1 to the destination Px from the information also stored in the storage unit 1604. The timing for obtaining the encounter probability may be periodic or may be when the encounter probability is acquired. FIG. 7 is a diagram showing an example of the actual risk potential and the encounter probability generated by the risk potential calculation unit 1606 and the encounter probability calculation unit 1607 of the prediction risk map generation unit 1605 using the accumulated information of the peripheral objects shown in FIG. Is. The actual risk potential of risk A is 100, the actual risk potential of risk B is 80, the actual risk potential of risk C is 50, the actual risk potential of risk D is 20, and the column of risk D is omitted in FIG. do.

As shown in FIG. 6, in lane 1 of the road section 0001, the object of risk A was detected twice out of three times of traveling, and the object of risk B was detected on each day of traveling three times. No other risk C and D objects were detected. Therefore, as shown in FIG. 7, the probability of encountering risk A in lane 1 of road section 0001 is 66% (= 2/3), the probability of encountering risk B is 100%, and the probability of encountering risks C and D is 0%. Is calculated as. Similarly, in lane 2 of the road section 0001, no objects at risks A to D were detected on any of the days as shown in FIG. Therefore, as shown in FIG. 7, the probability of encountering risks A to D in lane 2 of the road section 0001 is calculated as 0%. Further, as shown in FIG. 6, in the lanes 3 and 4 of the road section 0001, the object of risk B was detected in two of the three trips. Therefore, as shown in FIG. 7, the lane of the road section 0001. The probability of encountering risk B of 3 and 4 is calculated as 66% (= 2/3).

The predicted risk potential generation unit 1608 obtains the predicted risk potential by multiplying the actual risk potential for each lane for each road section divided in the extending direction of the road by a larger coefficient as the encounter probability increases and adding these. .. The road section is obtained for each road section divided by, for example, 100 m with respect to the extending direction of the road, and for a road having a plurality of lanes, it is obtained for each lane.

The coefficient for multiplying the actual risk potential is not particularly limited as long as the coefficient is larger as the encounter probability is larger, and the numerical value of the encounter probability expressed as a percentage may be multiplied as it is. For example, as shown in FIG. 7, in lane 1 of the road section 0001, the probability of encountering risk A (explicit risk potential is 100) is 66%, the probability of encountering risk B (explicit risk potential is 80) is 100%, and the risk. Since the encounter probability of C (explicit risk potential is 50) is 0%, the predicted risk potential is calculated as 100 × 66% + 80 × 100% + 50 × 0% = 14600. Similarly, as shown in FIG. 7, in lane 3 of the road section 0001, the probability of encountering risk A (explicit risk potential is 100) is 0%, and the probability of encountering risk B (explicit risk potential is 80) is 66%. Since the encounter probability of risk C (explicit risk potential is 50) is 0%, the predicted risk potential is calculated as 100 × 0% + 80 × 66% + 50 × 0% = 5280. In this way, the predicted risk potential obtained from the actual risk potential and the encounter probability is a value equal to or less than the actual risk potential.

In obtaining the value of the predicted risk potential, the avoidance time required to avoid the detected object is accumulated for each classification of the detected object, and the predicted risk potential is weighted by the ratio of the avoidance time for each classification. good. For example, an object that blocks a lane for a long time is a risk A (= 100), an object that temporarily blocks a lane is a risk B (= 80), an object that obstructs a traffic flow is a risk C (= 50), and a partial traffic Objects that obstruct the flow are classified as risk D (= 20), and the average time required to avoid each of the objects classified as risk A, B, C, and D is 10 minutes, 5 minutes, and 1 respectively. If it is minutes and 0.5 minutes, the values obtained by multiplying the risk potentials of risks A, B, C, and D by each encounter probability are multiplied by 10, 5, 1, and 0.5 as weights, respectively. , These may be added to obtain the predicted risk potential.

Further, in obtaining the value of the predicted risk potential, the encounter probability for each traveling position is the information of the time zone including the time when traveling from the current position P1 to the destination Px from the information stored in the storage unit 1604. May be extracted and obtained. Similarly, in obtaining the value of the predicted risk potential, the encounter probability for each traveling position has the same attribute of the date when traveling from the current position P1 to the destination Px from the information stored in the storage unit 1604. Information may be extracted and obtained. Similarly, when obtaining the value of the predicted risk potential, the encounter probability for each traveling position is the same as the operating status of the wiper when traveling from the current position P1 to the destination Px from the information stored in the storage unit 1604. You may obtain the information to be extracted.

Next, how to obtain the interrupted risk potential will be described. The interrupted risk potential generation unit 1609 obtains an interrupted risk potential that is lower than the predicted risk potential by using the predicted running motion of another vehicle that avoids the risk due to the predicted risk potential. Here, the risk due to the predicted risk potential means the risk identified by the risk potential and the encounter position used when the predicted risk potential generation unit 1608 obtains the predicted risk potential. That is, since the risk potential corresponds to the classified risks (for example, risks A to D), it is possible to grasp what kind of risk is encountered at which position on the travel route R from the risk potential and the encounter position. can. Further, the predicted running operation of the present embodiment is when a running vehicle encounters another vehicle stopped in the left lane or a risk of hindering running such as a traffic jam waiting for a right or left turn. , It means the running motion that is predicted to be taken in order to avoid the risk and continue running. Hereinafter, how to predict the running motion will be described by taking the predicted running motion in the running scene shown in FIG. 5 as an example.

FIG. 8 is a plan view showing an example of the predicted running operation of another vehicle that avoids the risk due to the predicted risk potential in the running scene shown in FIG. In FIG. 8, it is assumed that another vehicle exists as in the traveling scene shown in FIG. That is, there are other stopped vehicles V2a and V2b in the left lane of the road D1. In front of the other vehicles V2a and V2b, there is another vehicle V3a that is turning left at a low speed to enter the frontage road D3, and behind the other vehicle V3a is another vehicle waiting for the other vehicle V3a to turn left. There are V3b and V3c. Further, in the left lane of the road D1, there is a traffic jam of six other vehicles V4a to V4f waiting for a left turn at the intersection C. Further, in the right lane and the right turn dedicated lane of the road D1, there is congestion of nine other vehicles V5a to V5i waiting for a right turn. These vehicles fall under the risk due to the predicted risk potential. Here, in the driving scene shown in FIG. 8, another vehicle V2x traveling behind the other vehicles V2a and V2b, another vehicle V3x traveling behind the other vehicles V3a to V3c, and traveling behind the other vehicles V4a to V4f. It is assumed that there is another vehicle V4x and another vehicle V5x traveling behind the other vehicles V5a to V5i, and the other vehicles V2x, V3x, 4x and V5x are traveling along a route going straight through the intersection C. .. Further, in the traveling scene shown in FIG. 8, it is assumed that the own vehicle V1 is traveling straight in the central lane of the road D1.

In the traveling scene shown in FIG. 8, the other vehicle V2x traveling in the left lane of the road D1 cannot continue traveling in the left lane by encountering the other vehicles V2a and V2b that are stopped. Therefore, in order to avoid the stopped other vehicles V2a and V2b and continue traveling, the other vehicle V2x is, for example, behind the stopped other vehicle V2b from the left lane of the road D1 as shown in FIG. It is expected to change lanes to the central lane. That is, in this case, the predicted traveling operation of the other vehicle V2x that avoids the risk is a lane change from the left lane of the road D1 to the center lane behind the other vehicle V2b.

Further, in the traveling scene shown in FIG. 8, the other vehicle V3x traveling behind the other vehicle V3c encounters the traffic jam of the other vehicles V3a to V3c, with the other vehicle V3a trying to enter the side road D3 at the head. Then, in order to continue driving in the left lane, it is necessary to wait behind the other vehicle V3c until the traffic jam waiting for the left turn is cleared. Therefore, the other vehicle V3x avoids the congestion caused by the other vehicles V3a to V3c and continues to travel without waiting for the congestion to be resolved. Therefore, for example, as shown in FIG. 8, behind the other vehicle V3c, the road D1 It is predicted that the lane will change from the left lane to the center lane. That is, in this case, the predicted traveling operation of the other vehicle V3x that avoids the risk is a lane change from the left lane of the road D1 to the center lane behind the other vehicle V3c.

Similarly, in the driving scene shown in FIG. 8, the other vehicle V4x traveling behind the other vehicle V4f continues to travel in the left lane by encountering a traffic jam of the other vehicles V4a to V4f waiting for a left turn at the intersection C. In order to do so, it is necessary to wait behind the other vehicle V4e until the traffic jam waiting for the left turn is cleared. Therefore, the other vehicle V4x avoids the congestion caused by the other vehicles V4a to V4f and continues traveling without waiting for the congestion to be resolved. Therefore, for example, as shown in FIG. 8, behind the other vehicle V4f, the road D1 It is predicted that the lane will change from the left lane to the center lane. That is, in this case, the predicted traveling operation of the other vehicle V4x that avoids the risk is a lane change from the left lane of the road D1 to the center lane behind the other vehicle V4f.

Further, in the traveling scene shown in FIG. 8, the other vehicle V5x traveling behind the other vehicle V5i continues to travel in the right lane when it encounters a traffic jam of the other vehicles V5a to V5i waiting for a right turn at the intersection C. Therefore, it is necessary to wait behind another vehicle V5i until the traffic jam waiting for a right turn is cleared. Therefore, the other vehicle V5x avoids the congestion caused by the other vehicles V5a to V5i and continues to travel without waiting for the congestion to be resolved. Therefore, for example, as shown in FIG. 8, behind the other vehicle V5i, the road D1 It is predicted that the lane will change from the right lane to the center lane. That is, in this case, the predicted traveling operation of the other vehicle V5x that avoids the risk is a lane change from the right lane of the road D1 to the center lane behind the other vehicle V5i.

When the other vehicles V2x, V3x, V4x, and V5x change lanes from the left lane or the right lane to the center lane according to the predicted driving operation, the other vehicles V2x, V3x, V4x, and V5x are in the center lane in which the own vehicle V1 travels. , Will enter in front of the own vehicle V1. That is, the own vehicle V1 is the other vehicle V2x that avoids the other vehicles V2a and V2b that are stopped, and the other vehicle V3x that avoids the congestion of the other vehicles V3a to V3c led by the other vehicle V3a that is about to enter the side road D3. , Other vehicles V4x waiting for a left turn to avoid traffic jams, and other vehicles V5x waiting for a right turn to avoid traffic jams may be interrupted in front of the central lane in which the vehicle is traveling. be. The interrupted risk potential generation unit 1609 grasps the risk of being interrupted by other vehicles V2x, V3x, V4x, and V5x as a risk, and calculates the interrupted risk potential based on the risk.

When the risk of being interrupted by another vehicle V2x or the like is placed on the predicted risk map as the interrupted risk potential, the position where the interrupted risk potential is placed is, for example, the adjacent lane of the lane corresponding to the risk encounter position related to the predicted risk potential. Behind the position where the risk is encountered. For example, in the case of the risk of being interrupted by the other vehicle V2x shown in FIG. 8, the position where the other vehicle V2x encounters the risk is the position behind the other vehicle V2b stopped in the left lane of the road D1. The lane corresponding to the position behind the other vehicle V2b, that is, the lane adjacent to the left lane of the road D1, is the central lane of the road D1. Therefore, in this case, the interrupt risk potential derived from the traveling operation of avoiding the stopped other vehicles V2a and V2b is arranged behind the other vehicle V2b in the central lane of the road D1.

Further, in the case of the risk of being interrupted by the other vehicle V3x shown in FIG. 8, the position where the other vehicle V3x encounters the risk is the position behind the other vehicle V3c, which is the end of the traffic jam with the other vehicle V3a at the head. The lane corresponding to the position behind the other vehicle V3c, that is, the lane adjacent to the left lane of the road D1, is the central lane of the road D1. Therefore, in this case, the interrupted risk potential derived from the traveling operation for avoiding the congestion of the other vehicles V3a to V3c is arranged behind the other vehicle V3c in the central lane of the road D1.

Similarly, if there is a risk of being interrupted by the other vehicle V4x shown in FIG. 8, the position where the other vehicle V4x encounters the risk is the position behind the other vehicle V4f, which is the end of the traffic jam waiting for a left turn on the road D1. The lane corresponding to the position behind the other vehicle V4f, that is, the lane adjacent to the left lane of the road D1, is the central lane of the road D1. Therefore, in this case, the interrupted risk potential derived from the traveling operation of avoiding the left turn waiting congestion of the other vehicles V4a to V4f is arranged behind the other vehicle V4f in the central lane of the road D1.

Further, if there is a risk of being interrupted by the other vehicle V5x shown in FIG. 8, the position where the other vehicle V5x encounters the risk is the position behind the other vehicle V5i, which is the end of the traffic jam waiting for a right turn on the road D1. The lane corresponding to the position behind the other vehicle V5i, that is, the lane adjacent to the right lane of the road D1, is the central lane of the road D1. Therefore, in this case, the interrupted risk potential derived from the traveling operation of avoiding the right turn waiting congestion of the other vehicles V5a to V5i is arranged behind the other vehicle V5i in the central lane of the road D1.

In the lane adjacent to the lane corresponding to the position where the risk related to the predicted risk potential is encountered, the magnitude of the interrupted risk potential placed behind the position where the risk is encountered is calculated using the value of the predicted risk potential. be able to. This is because the interrupted risk potential is derived from the driving motion for avoiding the risk due to the predicted risk potential. The magnitude of the interrupted risk potential can be calculated, for example, as a value obtained by multiplying the value of the predicted risk potential corresponding to the risk to be avoided by a predetermined value (for example, 0.8). The predetermined value may be changed according to the risk classification, for example, 0.8 if the risk classification to be avoided is risk A, 0.6 if risk B, 0.4 if risk C, If the risk is D, it may be 0.2. For example, the predicted risk potential of lane 1 of the road section 0001 shown in FIG. 7 can be calculated as 100 × 66% + 80 × 100% + 50 × 0% = 14600. In this case, the interruption risk potential of the lane 2 of the road section 0001 can be calculated as, for example, 100 × 66% × 0.8 + 80 × 100% × 0.6 + 50 × 0% × 0.4 = 10080. Alternatively, or in addition to this, the magnitude of the interrupted risk potential may increase in proportion to the magnitude of the predicted risk potential corresponding to the risk to be avoided.

In addition, when it is caused by the same risk, the value of the interrupted risk potential becomes smaller than the value of the predicted risk potential. This is because the value of the interrupted risk potential is obtained by multiplying the predicted risk potential by a predetermined coefficient.

Further, the interrupted risk potential may be obtained for each lane of each road section divided in the extending direction of the road, similarly to the predicted risk potential. The road section is obtained for each road section divided by, for example, 100 m with respect to the extending direction of the road, and for a road having a plurality of lanes, it is obtained for each lane. Further, in the adjacent lane adjacent to the adjacent lane in which the interrupt risk potential is arranged, the interrupt risk potential may be used to obtain the second or higher interrupt risk potential.

As described above, the predicted risk map generation unit 1605 obtains the predicted risk potential and the interrupted risk potential for each traveling position, and generates a predicted risk map in which the predicted risk potential and the interrupted risk potential are expanded into map information. NS. FIG. 9 is a plan view showing an example of the predicted risk map generated by the predicted risk map generation unit 1605 of FIG. 3 for the traveling route R of FIG. In FIG. 9, it is shown that the darker the color attached to the lane, the larger the predicted risk potential and the interrupted risk potential. The darkest colored lane is the oncoming lane, which indicates that the own vehicle V1 cannot travel.

The lanes on which the own vehicle V1 that is not in the oncoming lane can run include a lane with the second darkest color, a lane with the third darkest color, and a lane with the fourth darkest color. There is a track with nothing attached. For example, in the left lane D11 of the road D1, the predicted risk potential obtained by the predicted risk potential generation unit 1608 is a large value due to frequent parking of other vehicles V2a and V2b on the road shoulder, so that the vehicle stops on the road shoulder. The position is the second darkest. Further, in the left lane D11 of the road D1, congestion due to other vehicles V3a to V3c frequently occurs due to the approach of the other vehicle V3a to the side road D3, so that the predicted risk potential obtained by the predicted risk potential generation unit 1608 is large. Since it is a value, the third darkest color is attached to the position where the traffic jam occurs. Similarly, in the left lane D11 of the road D1, traffic jams waiting for a left turn due to other vehicles V4a to V4f frequently occur, so that the predicted risk potential obtained by the predicted risk potential generation unit 1608 is a large value. , The third darkest color is attached to the position where the traffic jam occurs. Further, in the right lane D13 of the road D1, the predicted risk potential obtained by the predicted risk potential generation unit 1608 is a large value due to frequent traffic jams waiting for a right turn due to other vehicles V5a to V5i. The third darkest color is attached to the position where the traffic jam occurs.

On the other hand, for the central lane D12 of the road D1, the predicted risk potential related to the detected object is small. However, in the part of the central lane D12 near the intersection C, the risk potential in the sense that the own vehicle V1 must change lanes to the left lane D11 in order to turn left increases, so the second darkest color is used. It is attached. Similarly, the portion of the right lane D13 near the intersection C and the right turn dedicated lane D14 also have a large risk potential in the sense that the own vehicle V1 must change lanes to the left lane D11. Is darkly colored. In addition, the part of the road D1 that has passed the road D2 and cannot turn left on the road D2, the part of the intersection C where a vehicle turning right at the intersection C travels, and the second road D4 after turning right at the intersection C. It has a dark color. This is because these parts correspond to the positions where the own vehicle V1 cannot turn left at the intersection C, and the risk potential increases in the sense that the own vehicle V1 cannot enter the left lane D11 of the road D2. In addition, the frontage road D3 is also colored second darker. This is because when the own vehicle V1 enters the frontage road D3, the risk potential is high in the sense that the intersection C cannot be reached and the vehicle cannot travel along the travel route R.

In addition to these, in the central lane D12 of the road D1, the third darkest color corresponding to the predicted risk potential attached to the left lane D11 and the right lane D13 is followed by the fourth darkest color. .. This is because the interrupted risk potential obtained by the interrupted risk potential generation unit 1609 is a large value.

Returning to FIG. 3, the route calculation unit 160 of the present embodiment further includes a setting route reading unit 1610, a lane change necessity determination unit 1611, a lane change risk determination unit 1612, and a lane change risk potential generation unit 1613. .. Using these functions, the route calculation unit 160 of the present embodiment calculates the difficulty level of lane change according to the risk due to the risk potential arranged in the lane change destination lane as the lane change risk potential. By integrating the lane change risk potential with the predicted risk potential, it is possible to change lanes at an appropriate position while avoiding difficult lane changes. Each part will be described below.

The setting route reading unit 1610 reads the traveling route R set by the route planning unit 130 and sends it to the lane change necessity determination unit 1611. The lane change necessity determination unit 1611 compares the set travel path R with the current position P1 of the own vehicle V1 obtained from the detection device 1 and the own vehicle information detection device 4, the traveling lane of the own vehicle V1, and the like. Determine if a lane change is required to drive along the route. For example, when the next intersection C must be turned left in order to travel along the traveling route R, the own vehicle V1 determines the intersection C from the information obtained from the detection device 1 and the own vehicle information detection device 4. When it is detected that the vehicle is traveling in the right lane of a three-lane road heading toward, and it is determined that only the left lane of the road can turn left at the intersection C, the lane change necessity determination unit 1611 determines that the own vehicle V1 It is determined that it is necessary to change lanes from the right lane to the left lane before reaching the intersection C in order to travel along the traveling route R.

The lane change risk determination unit 1612 determines the risk associated with the driving operation of the lane change of the own vehicle V1 when the lane change necessity determination unit 1611 determines that the lane change is necessary, and further based on the risk. To determine the difficulty of changing lanes. The risk associated with the traveling operation of changing lanes in the present embodiment is the position where the own vehicle V1 has a lane to which the lane is changed (that is, an adjacent lane of the own lane) from the position where the own lane in which the own vehicle V1 travels. When changing lanes, the risk derived from an object detected in the lane of the lane change destination that the own vehicle V1 encounters shall be referred to. The lane change risk determination unit 1612 uses at least one of the actual risk potential or the predicted risk potential arranged in the lane of the lane change destination when determining the risk associated with the traveling operation of the lane change. Specifically, the classification of objects encountered in the lane of the lane change is grasped from the risk of at least one of the actual risk potential and the predicted risk potential arranged in the lane of the lane change destination. For grasping the classification of the object, the same method as in the case of calculating the interrupted risk potential in the interrupted risk potential generation unit 1609 can be used. The lane change risk determination unit 1612 assumes a driving scene when the own vehicle V1 changes lanes from the own lane to the lane to which the lane is changed, and classifies and self-classifies the objects encountered by the own vehicle V1 in the assumed driving scene. From the relationship with the vehicle V1, the influence of the object on the traveling operation of changing the lane of the own vehicle V1 is grasped. For example, a situation may occur in which the travel support device 100 cannot support the lane change of the own vehicle V1 due to the existence of the object and abandon the lane change, or the object exists again. Estimate whether it is necessary to change lanes. Then, based on this estimation result, when the own vehicle V1 changes lanes from the own lane to the adjacent lane, the certainty of whether or not the traveling support device 100 can complete the support for the traveling operation of the lane change is determined by the lane. Calculated as the difficulty of change. Hereinafter, how to determine the lane change risk will be described by taking the driving scene shown in FIG. 10 as an example.

FIG. 10 shows a plurality of traveling assumed when the own vehicle V1 turns left at the intersection C along the traveling route R in the traveling scene shown in FIG. 5 and changes lanes from the central lane of the road D1 to the left lane. It is a top view which shows the example of a scene. In FIG. 10, it is assumed that another vehicle exists as in the traveling scene shown in FIG. That is, there are other stopped vehicles V2a and V2b in the left lane of the road D1. In front of the other vehicles V2a and V2b, there is another vehicle V3a that is turning left at a low speed to enter the frontage road D3, and behind the other vehicle V3a is another vehicle waiting for the other vehicle V3a to turn left. There are V3b and V3c. Further, in the left lane of the road D1, there is a traffic jam of six other vehicles V4a to V4f waiting for a left turn at the intersection C. Further, in the right lane and the right turn dedicated lane of the road D1, there is congestion of nine other vehicles V5a to V5i waiting for a right turn. These vehicles fall under the risk due to the actual risk potential or the predicted risk potential.

In the traveling scene shown in FIG. 10, it is assumed that the own vehicle V1 changes lanes from the position of V1a in the central lane of the road D1 to the left lane, for example. In this case, the own vehicle V1 enters behind the other parked vehicle V2b in the left lane of the road D1. Here, the parked vehicle is classified as an object that blocks the lane for a long time, and as long as it is behind the other vehicle V2b, the own vehicle V1 cannot travel along the traveling route R. Therefore, the own vehicle V1 changes lanes again from the left lane of the road D1 to the central lane in order to travel along the traveling route R. Since the lane change again is a lane change from the rear of another stopped vehicle V2b, the own vehicle V1 changes lanes from a stopped state or a low vehicle speed state. Such a lane change may limit the support provided by the traveling support device because the vehicle speed difference from other vehicles approaching from behind becomes large. Further, after changing the lane to the central lane of the road D1, it is necessary to change the lane from the central lane of the road D1 to the left lane again within a limited distance to the intersection C. It may be necessary to change lanes at the driver's discretion. Therefore, the lane change risk determination unit 1612 determines that it is difficult to change lanes from the position V1a such that the vehicle enters behind the other parked vehicle V2b.

As another example, in the driving scene shown in FIG. 10, it is assumed that the own vehicle V1 changes lanes from the position of V1b in the central lane of the road D1 to the left lane, for example. In this case, the own vehicle V1 enters in front of the other parked vehicle V2a in the left lane of the road D1. Here, the parked vehicle is classified as an object that blocks the lane for a long time, and even if the own vehicle V1 enters the front of the other vehicle V2a, the other vehicle V2a may approach the own vehicle V1 from behind. Very Hard to think. Further, the own vehicle V1 will be located behind the other vehicle V3c after entering the left lane, but the other vehicles V3b and V3c waiting for the left turn of the other vehicle V3a are currently stopped. It is classified as an object that temporarily blocks the lane, where the traffic flow disappears over time. Therefore, if the congestion of the other vehicles V3a to V3c is awaited, the own vehicle V1 can continue to travel along the traveling route R without changing the lane from the left lane of the road D1 to the central lane again. can. Therefore, the lane change risk determination unit 1612 determines that the difficulty of changing lanes is low when changing lanes from the position V1b such that the vehicle enters in front of another parked vehicle V2a.

As an example similar to this, in the driving scene shown in FIG. 10, consider a case where the own vehicle V1 changes lanes from the position of V1c in the central lane of the road D1 to the left lane, for example. In this case as well, the own vehicle V1 is in front of the parked other vehicle V2a in the left lane of the road D1 and enters behind the other vehicle V3c. Therefore, as in the case of changing lanes from the position V1b, it is unlikely that the other vehicle V2a approaches the own vehicle V1 from behind, and if the congestion of the other vehicles V3a to V3c is awaited, the left lane of the road D1 is reached. It is possible to continue traveling along the traveling route R without changing the lane from the vehicle to the central lane again. However, compared to the case where the lane is changed from the position of V1b, the position of the own vehicle V1 after entering the left lane is closer to the other vehicle V3c, which is an object that temporarily blocks the lane, so the lane is changed. The behavior of the own vehicle V1 such as deceleration in the middle and after changing lanes may be larger than that in the case of changing lanes from position V1b. In consideration of this point, the lane change risk determination unit 1612 determines that the lane change from the position V1c is lower than the lane change from the position V1a, but higher than the difficulty of the lane change from the position V1b.

As another example, in the traveling scene shown in FIG. 10, it is assumed that the own vehicle V1 changes lanes from the position of V1d in the central lane of the road D1 to the left lane, for example. In this case, the own vehicle V1 enters in front of the other vehicle V3a that turns left on the side road D3 in the left lane of the road D1. The own vehicle V1 will be located behind the other vehicle V4f after entering the left lane, but the other vehicles V4a to V4f waiting for a left turn are currently stopped, but if time passes. It is classified as an object that temporarily blocks the lane, which eliminates traffic flow. Therefore, if the congestion of the other vehicles V4a to V4f is awaited, the own vehicle V1 can continue to travel along the traveling route R without changing the lane from the left lane of the road D1 to the central lane again. can. However, when the left turn of the other vehicle V3a is completed, it is considered that the other vehicles V3b and V3c, which are objects that temporarily block the lane, resume traveling in the left lane of the road D1 by eliminating the traffic jam. At this time, the own vehicle V1 that has entered the front of the other vehicle V3a may be approached by the other vehicles V3b and V3c from behind. In consideration of this point, the lane change risk determination unit 1612 indicates that the lane change from the position V1d such that the vehicle enters in front of the other vehicle V3a, which is the leading vehicle in the traffic jam, is lower than the lane change from the position V1a, but the position V1b. Judged as higher than the difficulty of changing lanes from.

As yet another example, in the driving scene shown in FIG. 10, it is assumed that the own vehicle V1 changes lanes from the position of V1e in the central lane of the road D1 to the left lane, for example. In this case, the own vehicle V1 changes lanes while interrupting the lanes of other vehicles V4a to V4f waiting for a left turn in the left lane of the road D1. Here, in order to change lanes with an interruption to the lane of traffic jam waiting for a left turn, which is an object that temporarily blocks the lane, the own vehicle V1 must stop in a traveling lane such as the central lane of road D1, and the vehicle V1 must stop behind. Other vehicles with high vehicle speed may approach. In addition, the inter-vehicle distance in a congested vehicle line waiting for a left turn may be short, and if the inter-vehicle distance is short, the support provided by the traveling support device may be limited. Therefore, the lane change risk determination unit 1612 determines that the difficulty of changing lanes is high when changing lanes from the position V1e that interrupts the lane of traffic jam waiting for a left turn.

The example shown in FIG. 10 assumes a driving scene in which the lane is changed to the left lane when turning left, but also in a driving scene in which the lane is changed to the right lane when turning right, as in these examples, when changing lanes. Assuming a driving scene, the difficulty of changing lanes can be determined as the risk of changing lanes. For example, in the driving scene shown in FIG. 10, if the own vehicle V1 changes lanes from the position of V1e in the center lane of the road D1 to the right lane, the own vehicle V1 waits for a right turn in the right lane of the road D1. The lane is changed while interrupting the lanes of other vehicles V5a to V5i. In this case, the lane change risk determination unit 1612 needs to interrupt the lane change waiting for a right turn, which is an object that temporarily blocks the lane, because the lane change from the position V1e to the right lane is required. It is judged that the difficulty level of is high. In addition, when the own vehicle travels on a road having three or more lanes and it is determined that multiple lane changes are required, the risk of lane change is determined for each lane change by the same method as described above. can do.

Then, the lane change risk potential generation unit 1613 calculates the lane change risk potential using the difficulty level of the lane change determined by the lane change risk determination unit 1612. Specifically, at each position of the own lane in which the own vehicle V1 travels, the lane change risk potential determined by the lane change risk determination unit 1612 is determined according to the difficulty of changing the lane from the position to the adjacent lane. calculate. The lane change risk potential becomes higher as the difficulty of changing lanes, that is, the higher the risk associated with the driving operation of changing lanes of the own vehicle V1. That is, a high lane change risk potential is arranged at a position where the difficulty of changing lanes is high in the own lane, and a low lane change risk potential is arranged at a position where the difficulty of changing lanes is low in the own lane. The action determination unit 1617 of FIG. 3, which will be described later, calculates the traveling route of the own vehicle V1 so as to change the lane at a position where the lane change risk potential is low in the own lane.

The lane change risk potential calculated by the lane change risk potential generation unit 1613 can be integrated into the risk map generated by the prediction risk map generation unit 1605. The traveling support device 100 of the present embodiment adds the lane change risk potential to the predicted risk potential of the own lane in which the own vehicle V1 travels to obtain the risk potential of the own lane, and uses the risk potential of the own lane to obtain the own vehicle. Set the position where V1 changes lanes. An example of such a risk potential of the own lane is shown in FIG.

FIG. 11 is a risk map in which the predicted risk map of FIG. 9 is integrated with the lane change risk potential calculated by the lane change risk potential generation unit 1613. The risk map shown in FIG. 11 is a risk map of FIG. 9 in that a lane change risk potential calculated using the predicted risk potential arranged in the left lane D11 of the road D1 is arranged in the central lane D12 of the road D1. Different from. For lanes other than the central lane D12, the risk potential of FIG. 11 is the same as that of FIG.

When the lane change risk potential of the present embodiment is arranged in the own lane in which the own vehicle V1 travels, it is arranged in a position adjacent to the position in the own lane where the predicted risk potential existing in the lane to be changed is not 0. In addition to this, in the own lane in which the own vehicle V1 travels, the lane change risk potential can be arranged at a position adjacent to the position where the predicted risk potential existing in the lane to be changed is 0. In particular, when the object to be encountered is an object that blocks the lane for a long time, the lane change risk potential is set to a position adjacent to the predicted risk potential and a position behind the predicted risk potential in the own lane in which the own vehicle V1 is traveling. Place in and. For example, in the risk map of FIG. 11, in the left lane D11 of the road D1, the predicted risk potential is arranged at a position corresponding to the position where the other parked vehicles V2a to V2b, which are objects that block the lane for a long time, are encountered. However, with respect to this predicted risk potential, a lane change risk potential is arranged in the central lane D12 of the road D1 at a position adjacent to the predicted risk potential arranged in the left lane D11. In addition to this, in the risk map of FIG. 11, in the left lane D11 of the road D1, the predicted risk potential is not arranged behind the position where the other vehicles V2a and V2b are encountered (that is, the risk potential is 0). However, in the central lane D12, the lane change risk potential is arranged at a position adjacent to the position behind the other vehicle V2b.

Further, when the object to be encountered is an object that temporarily blocks the lane, an object that obstructs the traffic flow, or an object that partially obstructs the traffic flow, the own vehicle V1 travels on the lane change risk potential. In the lane, the position is adjacent to the position where the predicted risk potential is not 0, and the position is located in front of and / or behind the position. For example, in the risk map of FIG. 11, a position corresponding to a position in the left lane D11 of the road D1 that encounters a lane of other vehicles V3a to V3c that are congested waiting for a left turn, which is an object that temporarily blocks the lane. In contrast to this predicted risk potential, a lane change risk potential is placed in the central lane D12 of the road D1 at a position adjacent to the predicted risk potential placed in the left lane D11. ing. In addition to this, in the risk map of FIG. 11, in the left lane D11 of the road D1, the predicted risk potential is not arranged in front of and behind the lanes of other vehicles V3a to V3c (that is, the risk potential is 0). ) Arranges the lane change risk potential at a position adjacent to the position in front of the other vehicle V3a and a position adjacent to the position behind the other vehicle V3c in the central lane D12.

Similarly, in the risk map of FIG. 11, in the left lane D11 of the road D1, at a position where the vehicle line of other vehicles V4a to V4f, which is an object that temporarily blocks the lane and is congested waiting for a left turn, is encountered. The predicted risk potential is arranged at the corresponding position. In contrast to this predicted risk potential, the central lane D12 of the road D1 has a lane change risk potential at a position adjacent to the predicted risk potential arranged in the left lane D11. Is placed. In addition to this, in the risk map of FIG. 11, in the left lane D11 of the road D1, the predicted risk potential is not arranged behind the lanes of other vehicles V4a to V4f (that is, the risk potential is 0). , In the central lane D12, the lane change risk potential is arranged at a position adjacent to a position adjacent to the position in front of the other vehicle V4f.

In the central lane D12 of the road D1 of the risk map shown in FIG. 11, in addition to the predicted risk potential and the interrupted risk potential, the value of the risk potential is high at the above-mentioned position where the lane change risk potential is arranged. The position of is the second darkest color. Here, in the risk map shown in FIG. 11, looking at the risk potential of the central lane D12 of the road D1, the risk potential at the position adjacent to the position in front of the position where the other vehicle V2a is encountered is compared with the other positions. The value of is low. The action determination unit 1617, which will be described later, searches for a position where the value of this risk potential is low, and sets the position as a position for changing lanes. Further, although shown by shades of color in FIG. 11, the magnitude of the lane change risk potential is determined by using the value of the predicted risk potential placed at the lane change destination to avoid difficult lane changes and change lanes. It is possible to set an appropriate value so as to suppress the risk associated with the running operation of. For example, the value of the predicted risk potential arranged at the lane change destination may be multiplied by a coefficient (for example, 1.5) to obtain the value of the lane change risk potential.

Based on the set lane change position, the action determination unit 1617 in FIG. 3 refers to the risk map shown in FIG. 11, and when traveling on the travel routes R1 and R2 in FIG. 4, the risk potential becomes the smallest. Select a driving route. Here, when traveling on the traveling paths R1 and R2 of FIG. 4, it is not necessary to travel in the vicinity of the traveling position (near the encounter position with the detected object), that is, in the same lane, and the case of traveling on the same road is included. Further, the processor 10 calculates the predicted risk potential, the interruption risk potential, and the lane change risk potential at least before traveling at the traveling position (the encounter position with the detected object).

The travel routes R1a and R2a shown in FIGS. 12A to 12C are risk maps shown in FIG. 11 in consideration of the predicted risk potential, the interruption risk potential, and the lane change risk potential when traveling on the travel routes R1 and R2 of FIG. This is the travel route finally set by the action determination unit 1617 with reference to.

In the traveling route R1a shown in FIG. 12A, the own vehicle V1 changes lanes from the central lane of the road D1 to the left lane so as to enter in front of the other parked vehicle V2a. As a result, it is possible to change lanes at a position where the difficulty of changing lanes is low, that is, at a position where the risk associated with the traveling operation of changing lanes is low. After the lane change, the own vehicle V1 will be located behind the other vehicles V3a to V3c that are congested, but the traffic congestion caused by waiting for the other vehicle V3a to turn left on the side road D3 without changing the lane again. It is assumed that the vehicle waits behind the other vehicle V3c until the problem is resolved.

When the left turn of the other vehicle V3a to the side road D3 is completed and the traffic congestion caused by the left turn is completed, the own vehicle V1 is behind the other vehicles V3b and V3c and at the intersection C along the traveling route R1b shown in FIG. 12B. Go straight on the left lane of road D1 toward. In the driving scene shown in FIG. 12B, while waiting for the completion of the left turn waiting for the other vehicle V3a on the side road D3, the traffic jam waiting for the left turn of the other vehicles V4a to V4f is also eliminated, and the other vehicles V4a to V4f make a left turn at the intersection C. It is assumed that it has been completed. Therefore, the own vehicle V1 does not have to wait for the traffic jam to be cleared in order to turn left at the intersection C. In the driving scene shown in FIG. 12B, it is assumed that the traffic jam waiting for the right turn in the right lane and the right turn dedicated lane on the road D1 has been eliminated, and the other vehicles V5a to V5f have completed the right turn at the intersection C. The travel route R2a shown in FIG. 12C is a travel route when the own vehicle V1 turns left at the intersection C. Following the other vehicle V3c, the own vehicle V1 will turn left at the intersection C.

On the other hand, when traveling on the traveling routes R1 and R2 in FIG. 4, refer to the predicted risk map shown in FIG. 9 in consideration of the predicted risk potential and the interrupted risk potential without considering the lane change risk potential. FIG. 16 shows a travel path R1x finally set by the action determination unit 1617.

In the traveling route R1x shown in FIG. 16, the own vehicle V1 changes lanes from the central lane of the road D1 to the left lane so as to enter in front of the other vehicle V3a while turning left. This lane change is a risk compared to the lane change of the traveling route R1a because when the left turn of the other vehicle V3a is completed, the other vehicles V3b and V3c that have cleared the traffic jam will resume running and approach from behind. Is a high lane change. Further, if the left turn of the other vehicle V3a is completed before the own vehicle V1 moves to the rear of the other vehicle V3a from the current position P1 in the left lane of the road D1, the own vehicle V1 moves to the left lane. You cannot change lanes. This is because when the traffic jam is eliminated, the other vehicles V3b and V3c resume running, and there is no space for the own vehicle V1 to enter the left lane of the road D1. In this case, the own vehicle V1 abandons the lane change to the left lane and cannot continue the traveling support along the traveling route.

Separately from this, the action determination unit 1617 referring to the predicted risk map shown in FIG. 9 may set the traveling route R1y shown in FIG. In the traveling route R1y, the own vehicle V1 must change lanes while interrupting the other vehicles V4a to V4f waiting for a left turn. For such a difficult (that is, high-risk) lane change, the support provided by the traveling support device 100 may be limited, and another vehicle approaching from behind may be encountered. As a result, there is a high possibility that the lane change to the left lane will be abandoned and it will not be possible to continue driving support along the driving route.

In this way, by using the lane change risk potential, the position of the lane change is set after considering the difficulty of the driving operation of the lane change, which was not considered by the predicted risk potential and the interrupted risk potential. Will be able to. That is, by using the lane change risk potential, a position where the other vehicle V4a to V4f interrupts the congested vehicle line waiting for a left turn, or a position where the other vehicle V3b approaches from behind after the other vehicle V3a enters the side road D3. Therefore, it is possible to prevent the own vehicle V1 from changing lanes. As a result, it is possible to avoid the occurrence of a situation in which the own vehicle V1 gives up changing lanes and changes the traveling route.

Here, in order to reflect the difficulty of changing lanes for each classification of risks encountered, the magnitude of the lane change risk potential is the position adjacent to the predicted risk potential for an object that blocks the lane for a long time in the own lane. Is set to the maximum, and the position behind the position adjacent to the predicted risk potential for the object that blocks the lane for a long time and the position adjacent to the predicted risk potential for the object that temporarily blocks the lane are set to the next largest. However, the positions in front of and behind the position adjacent to the predicted risk potential of the object that temporarily blocks the lane may be set to the minimum. Alternatively or additionally, if the object encountered is a convoy waiting for a right or left turn, the lane change risk potential may be set higher as it is closer to the right or left turn position.

Further, in the case of arranging the lane change risk potential in the own lane in which the own vehicle V1 is traveling, the lane change risk potential is arranged at a position adjacent to the position where the predicted risk potential existing in the lane change destination lane is 0. , The value of the lane change risk potential may be set smaller as the distance from the position where the predicted risk potential is not 0. In particular, when the object encountered is an object that temporarily blocks the lane, an object that obstructs the traffic flow, or an object that partially obstructs the traffic flow, the position adjacent to the position where the risk potential is not 0 in the own lane. The lane change risk potential arranged at the front and / or rear positions is set to be smaller as the distance from the position where the predicted risk potential is not 0. When reducing the lane change risk potential, the ratio of reduction can be set to an appropriate value according to the object to be encountered.

In the own lane, the magnitude of the reduction ratio of the lane change risk potential arranged at the position behind the position adjacent to the position where the risk potential is not 0 is when the object is an object that partially obstructs the traffic flow. Is the largest for vehicles in adjacent lanes, as they may slow down but may be moving, and the amount of slowdown in their vehicle is even smaller, if the object is an object that obstructs traffic flow. , Vehicles in adjacent lanes may be moving at low speeds, own vehicles do not need to slow down to 0, then become larger, and if the object is an object that temporarily blocks the lane, it is adjacent Vehicles in the lane are likely to be stopped, and the own vehicle needs to reduce the vehicle speed to 0 while changing lanes, which is the smallest.

On the other hand, in the own lane, the magnitude of the reduction ratio of the lane change risk potential arranged at the position in front of the position adjacent to the position where the risk potential is not 0 is the case where the object temporarily blocks the lane. It is the largest when the object is an object that obstructs the traffic flow, and is the smallest when the object is an object that partially obstructs the traffic flow. That is, compared to the case where the object temporarily blocks the lane, when the object is an object that obstructs the traffic flow or an object that partially obstructs the traffic flow, even if the distance from the object is increased. It is difficult to reduce the value of risk potential. This is because objects that temporarily block a lane, such as a congested lane, often travel at low speeds, so the risk can be avoided even if the distance to the object is short, whereas pedestrians walking in the lane, In the case of an object such as a bicycle or a two-wheeled vehicle that partially obstructs the traffic flow, it may be possible to continue running by avoiding it in the lateral direction. This is because it may approach V1 and a distance from the object is required to avoid the risk.

Alternatively or in addition to this, the reduction ratio may be set smaller as the speed limit of the road on which the own vehicle V1 travels is higher. Further, instead of or in addition to this, the reduction ratio may be set to be smaller as the vehicle speed of the own vehicle V1 is higher. This is because the higher the vehicle speed limit or the traveling vehicle speed, the longer the braking distance of the vehicle required to avoid the risk.

In addition, instead of or in addition to this, the lane change risk potential is the highest between the current position P1 of the own vehicle V1 and the next right turn or left turn position as a section for setting the lane change position. It may be set to change lanes at a smaller position. Further, instead of or in addition to this, when setting a traveling route, if there are a plurality of routes from the current position P1 of the own vehicle V1 to the destination Px, there is a risk of changing lanes arranged in the route. The route with the smallest sum of potentials may be selected.

Returning to FIG. 3, the route calculation unit 160 of the present embodiment further includes an actual risk map learning unit 1614, an actual risk map generation unit 1615, and a risk map integration unit 1616.

The manifest risk map learning unit 1614 generates a locus-guided potential for generating the manifest risk map. When a human driver sees an object, such as a parked vehicle, in a traffic environment, he or she does not know how far away from it, but what to do or what route to take to handle it. think. In order to imitate such a mechanism, the manifest risk map learning unit 1614 generates a locus guidance potential for displaying the manifest risk map from the driving data. That is, a locus guidance potential is generated in real time for each classification of each object actually detected, for example, a vehicle, a pedestrian, or a bicycle. This includes a repulsive space potential for collision prevention, a suction space potential for guiding a desired trajectory, and a velocity potential for inducing an appropriate target velocity. In addition, the probe car learns its trajectory with natural driving data as it deals with various traffic participants in each category. In online processing, the locus-guided potential is used to calculate the desired local locus and target velocity profile.

The actual risk map generation unit 1615 is based on the trajectory guidance potential learned in advance for each classification obtained from the actual risk map learning unit 1614 and the classification result of the traffic participants around the own vehicle V1. An actual risk map is generated by applying the trajectory guidance potential according to the classification for each traffic participant around the vehicle. The predicted risk map generated by the predicted risk map generation unit 1605 predicts the risk potential based on the experience so far using the characteristic value of the encounter probability, whereas the actual risk map generation unit 1615 predicts the risk potential. The actual risk map generated in the above is a calculation of the risk potential for an object detected while actually traveling on a traveling route. As a result, if an object is accidentally or suddenly detected in a road section or lane where the predicted risk potential and the interrupted risk potential are low due to a low probability of encountering the detected object, etc., it is based on the actual risk potential. Appropriate driving assistance can be provided.

When the same risk is caused, the actual risk potential calculated for the detected risk is larger than the predicted risk potential for the risk. This is because the predicted risk potential is the risk potential predicted by using the characteristic value of the encounter probability with respect to the risk potential.

The risk map integration unit 1616 is generated by the predicted risk map generated by the predicted risk map generation unit 1605, the actual risk map generated by the actual risk map generation unit 1615, and the lane change risk potential generation unit 1613. Generate an integrated risk map that integrates with the lane change risk potential. Specifically, the risk map integration unit 1616 detects an object around the own vehicle V1 when actually traveling the own vehicle V1, and when an obstacle or other object is detected, the actual risk map generation unit The actual risk potential of the object detected by 1615 is obtained. Then, the predicted risk potential and the interrupted risk potential are compared with the actual risk potential, and based on the risk potential of the larger risk potential, the vehicle is supported to run, such as setting the lane in which the own vehicle V1 travels. To generate an integrated risk map. At this time, if the lane change necessity determination unit 1611 determines that the lane change is necessary for turning right or left, and the lane change risk potential generation unit 1613 calculates the lane change risk potential, In the integrated risk map, the lane change risk potential is added to the actual risk potential, the predicted risk potential, and the interrupted risk potential arranged in the own lane in which the own vehicle V1 travels.

For example, if other vehicles V4a to V4f waiting for a left turn in the left lane are detected when actually traveling on the road D1 along the travel route R1, the actual risk map generation unit 1615 will be in the other vehicles V4a to V4f. Calculate the actual risk potential and generate an actual risk map. Then, the risk map integration unit 1616 integrates the actual risk map and the predicted risk map of FIG. 11, and generates, for example, the integrated risk map shown in FIG. In the integrated risk map shown in FIG. 13, compared with the predicted risk map shown in FIG. 11, the vehicle is arranged at a position corresponding to the position of the convoy of other vehicles V4a to V4f waiting for a left turn in the left lane of the road D1. The color of the risk potential is getting darker. This is because the risk potentials placed at the positions corresponding to the positions of the other vehicles V4a to V4f are changed from the predicted risk potentials to the actual risk potentials by actually detecting the other vehicles V4a to V4f, and the value of the risk potentials is changed. Because it became expensive.

Further, for example, when actually traveling on the road D1 along the traveling route R1, another vehicle V4x traveling in the left lane toward the intersection C is detected behind the other vehicles V4a to V4f waiting for a left turn in the left lane. Then, in the integrated risk map shown in FIG. 17, the risk potential arranged at the position behind the other vehicles V4a to V4f in the central lane of the road D1 may be weighted. For example, when another vehicle V4x is actually detected, the risk potential arranged at a position behind the other vehicles V4a to V4f in the central lane of the road D1 may be multiplied by 1.5. As a result, when the other vehicle V4x that avoids the risk due to the predicted risk potential is actually detected, the risk based on the driving motion in which the other vehicle V4x avoids the other vehicles V4a to V4f is appropriately evaluated and avoided in advance. Can be done.

Next, the processing contents executed by the route calculation unit 160 will be described. FIG. 14 is a flowchart showing information processing procedures in the peripheral object locus acquisition unit 1601, the peripheral object classification unit 1602, the peripheral object information storage unit 1603, and the storage unit 1604 of the route calculation unit 160. FIGS. 15A and 15B are routes. It is a flowchart which shows the information processing procedure in the prediction risk map generation unit 1605 of the calculation unit 160, the manifest risk map learning unit 1614, the manifest risk map generation unit 1615, the risk map integration unit 1616, and the action decision unit 1617.

First, when each vehicle travels on an arbitrary road, the process shown in FIG. 14 is executed, and the data accumulated by this is used for subsequent traveling support for each vehicle. In step S11 of FIG. 14, the processor 10 determines whether or not each vehicle has started traveling, and when the vehicle has started traveling, the process proceeds to step S12, and the peripheral area is used by using a detection device 1 such as an image pickup device or a distance measuring device. Detect an object. If the vehicle has not started traveling, the processor 10 repeats step S11.

In step S12, when an object around the own vehicle V1 is detected, the processor 10 proceeds to step S13, classifies the detected objects using the environment recognition device 5 and the object recognition device 6, and detects the own vehicle information. The position information of the position where the object is detected is acquired by using the device 4. Then, the processor 10 associates the classification related to the actual risk potential of the detected object with the detected position and stores it in the storage unit 1604. In step S14, the processor 10 determines whether or not the traveling of the own vehicle V1 has been completed, and if not, returns to step S12 to repeat the detection of the object and the accumulation of data until the traveling is completed. By accumulating a large amount of data in which the object according to the classification and the position information are associated with each other in the storage unit 1604, it is possible to obtain empirical risk potential data at an arbitrary position.

Next, when starting the running support of the own vehicle V1, the processes shown in FIGS. 15A and 15B are executed. The driving support of the present embodiment is assumed to be driving support in which the driver inputs the destination Px and autonomously controls the driving using the traveling path R from the current position P1 to the destination Px. In addition to the final destination, the destination Px includes an intermediate point and an intersection to be encountered next, for example, an intersection where a left turn is scheduled. In this case, first, in step S21 of FIG. 17A, the processor 10 determines whether or not the traveling support of the own vehicle V1 has started, and if so, proceeds to step S22. If the driving support has not started, the processor 10 repeats step S21. In step S22, the processor 10 prompts the driver to input the destination Px, acquires the current position P1 of the own vehicle V1 by the own vehicle information detection device 4, and acquires the destination Px input by the driver.

In step S23, the processor 10 calculates the travel path R based on the current position P1 and the destination Px of the own vehicle V1 acquired in step S22. In the following step S24, the processor 10 acquires the actual risk potential for each traveling position (that is, for each road section and each lane) of the traveling route R calculated in step S23 from the storage unit 1604. Further, in the following step S25, the processor 10 acquires the probability of encountering an object for each traveling position (that is, for each road section and for each lane) of the traveling route R calculated in step S23 from the storage unit 1604. Then, in the following step S26, the processor 10 calculates the predicted risk potential by multiplying the actual risk potential of each of the detected objects acquired in steps S23 and S24 with the encounter probability. After calculating the predicted risk potential in step S26, in step S27, the processor 10 calculates the interrupted risk potential using the actual risk potential and the encounter position used in the calculation of the predicted risk potential. After starting the traveling support of the own vehicle V1, preferably, the processes of steps S22 to S27 are executed before the traveling is started, and the road section and the lane in which the predicted risk potential and the interrupted risk potential are the smallest are selected. Set the route R.

When the own vehicle V1 starts traveling along the travel path R, the processor 10 detects surrounding objects in real time in step S28 of FIG. 15B. If no object is detected, the process proceeds to step S30. On the other hand, when an object is detected, the process proceeds to step S29, and the processor 10 calculates the actual risk potential of the object detected by the actual risk map generation unit 1615. In step S30, whether or not the processor 10 needs to change lanes in order to travel along the set route by the lane change necessity determination unit 1611 for the set route read by the set route reading unit 1610. To judge. If it is determined that it is necessary to change lanes in order to travel along the set route, the process proceeds to step S31. On the other hand, if it is determined that it is not necessary to change lanes in order to travel along the set route, the process proceeds to step S32.

When the process proceeds from step S30 to step S31, the processor 10 determines whether or not the predicted risk potential or the interrupted risk potential is arranged in the lane ahead of the lane change by the lane change risk determination unit 1612. To judge. If it is determined that the predicted risk potential or the interrupted risk potential is arranged in the lane ahead of the own vehicle V1 changing lanes, the process proceeds to step S35. On the other hand, if it is determined that the predicted risk potential is interrupted and the risk potential is not arranged in the lane ahead of the own vehicle V1 changing lanes, the process proceeds to step S32.

When the process proceeds from step S31 to step S35, the processor 10 calculates the lane change risk potential by the lane change risk potential generation unit 1613. In the following step S36, the processor 10 sets the position where the own vehicle V1 changes lanes to the target lane by using the lane change risk potential calculated in step S35. Then, in the following step S37, the processor 10 supports the running of the own vehicle V1 by using the actual risk potential, the predicted risk potential, and the interrupted risk potential so as to change the lane at the lane change position set in the step S36. ..

On the other hand, when the process proceeds from step S30 or step S31 to step S32, the processor 10 determines whether or not the actual risk potential is equal to or greater than the predicted risk potential and the interrupted risk potential. If the actual risk potential is equal to or greater than the predicted risk potential and the interrupted risk potential, the process proceeds to step S33, and the processor 10 uses the actual risk potential to support the running of the own vehicle V1. On the other hand, if the actual risk potential is less than the predicted risk potential and the interrupted risk potential, the process proceeds to step S34, and the processor 10 supports the running of the own vehicle V1 using the predicted risk potential and the interrupted risk potential. do. If no surrounding object is detected in step S28 and the actual risk potential is not calculated in step S29, the actual risk potential is set to 0 and the determination in step S32 is made. That is, when the actual risk potential is not calculated, it is always determined that the predicted risk potential is larger than the actual risk potential, and the process proceeds to step S34.

The calculation of the interrupted risk potential in step S27 can be omitted if necessary. In this case, the processor 10 interrupts in steps S28 to S37 and performs processing without using the risk potential.

As described above, according to the vehicle traveling support method and the support device of the present embodiment, when an object is detected by the vehicle, the risk potential of the object is obtained, and the risk potential of the object and the encounter position where the object is encountered are determined. Correspondingly, the risk potential at the encounter position is accumulated as the actual risk potential, and the accumulated actual risk potential at the encounter position is used to encounter the encounter at the encounter position, which is lower than the risk potential obtained when the object is detected. When the predicted risk potential of the predicted object is obtained and the encounter position is re-traveled when traveling along the route to the destination Px, the traveling of the vehicle is autonomously controlled using the predicted risk potential. Here, it is determined whether or not it is necessary to change the lane of the own vehicle V1 in order to travel along the route, and if it is determined that the lane change is necessary, at least one of the actual risk potential and the predicted risk potential. Using either one, calculate the lane change risk potential due to the risk associated with the driving operation of the lane change, and add the lane change risk potential to the predicted risk potential of the own lane V1 in which the own vehicle V1 travels, and add the risk potential of the own lane. And set the position of the lane change using the risk potential of the own lane. As a result, the position of the lane change can be set in consideration of the difficulty of the driving operation of the lane change, which the predicted risk potential and the interrupted risk potential are not considered. That is, the position where the other vehicle V2b is parked, the position where the other vehicle V4a to V4f interrupts the congested vehicle line waiting for a left turn, and the position where the other vehicle V3a enters the side road D3 and then the other vehicle V3b approaches from behind. It is possible to prevent the own vehicle V1 from changing lanes at such a position. As a result, it is possible to avoid the occurrence of a situation in which the own vehicle V1 gives up changing lanes and changes the traveling route.

Further, according to the vehicle traveling support method and the support device of the present embodiment, the lane change risk potential is arranged at a position adjacent to a position in the own lane where the predicted risk potential existing in the lane to be changed is not 0. .. As a result, it is possible to prevent the own vehicle V1 from changing lanes toward the position where the other vehicle V2 is parked and the congested lane that temporarily blocks the lane.

Further, according to the vehicle traveling support method and the support device of the present embodiment, the lane change risk potential is arranged at a position adjacent to the position in the own lane where the predicted risk potential existing in the lane to be changed is 0. However, the lane change risk potential is set smaller as the distance from the position where the predicted risk potential is not 0 becomes smaller. As a result, there is a risk that the lane will need to be changed again by changing the lane to the rear of the other vehicle V2b that blocks the lane for a long time, and changing the lane to a position closer to the other vehicle V3c that temporarily blocks the lane. With the driving operation of changing lanes, such as the risk that the behavior of the own vehicle V1 will become large, and the risk that other vehicles V3b and V3c, which have temporarily blocked the lane due to the elimination of congestion, will approach the own vehicle V1 from behind. Risks can be considered appropriately.

Further, according to the vehicle traveling support method and the support device of the present embodiment, the objects encountered are objects that block the lane for a long time, objects that temporarily block the lane, objects that obstruct the traffic flow, or partially. It is an object that obstructs the traffic flow. This makes it possible to set an appropriate lane change risk potential for each classification of the objects encountered.

Further, according to the vehicle traveling support method and the support device of the present embodiment, the lane change risk potential has the largest position in the own lane adjacent to the predicted risk potential of an object that blocks the lane for a long time, and the lane is changed. An object that has the next largest position behind the position adjacent to the predicted risk potential for an object that is blocked for a long time and the position adjacent to the predicted risk potential for an object that temporarily blocks the lane, and temporarily blocks the lane. The positions in front of and behind the positions adjacent to the predicted risk potential related to the above are the smallest. This makes it possible to set an appropriate lane change risk potential for each classification of the objects encountered.

Further, according to the vehicle traveling support method and the support device of the present embodiment, when the encountering object is an object that blocks the lane for a long time, the lane change risk potential is adjacent to the predicted risk potential in the own lane. It is arranged at a position and a position behind the position. As a result, it is possible to avoid the risk of having to change lanes again by changing the lane to the rear of the other vehicle V2b that blocks the lane for a long time.

Further, according to the vehicle traveling support method and the support device of the present embodiment, when the encountering object is an object that temporarily blocks the lane, an object that obstructs the traffic flow, or an object that partially obstructs the traffic flow. , The lane change risk potential is arranged at a position adjacent to a position where the predicted risk potential is not 0 in the own lane and at a position in front of and / or behind the position. As a result, there is a risk that the behavior of the own vehicle V1 will increase by changing the lane to a position closer to the other vehicle V3c that temporarily blocks the lane. It is possible to appropriately consider the risks associated with the traveling operation of changing lanes, such as the risk that the vehicles V3b and V3c approach the own vehicle V1 from behind.

Further, according to the vehicle traveling support method and the support device of the present embodiment, when the encountering object is an object that temporarily blocks the lane, an object that obstructs the traffic flow, or an object that partially obstructs the traffic flow. , When arranging the lane change risk potential in the position adjacent to the position where the predicted risk potential is not 0 in the own lane and the position in front of and / or behind the position, the position is in the front and / or rear position. The arranged lane change risk potential is set smaller as the distance from the position where the predicted risk potential is not 0 is set. As a result, the relationship between the distance from the risk due to the predicted risk potential and the difficulty level of the lane change can be reflected in the value of the lane change risk potential.

Further, according to the vehicle traveling support method and the support device of the present embodiment, when the lane change risk potential is set to be smaller so as to move backward in the traveling direction from a position where the predicted risk potential is not 0, the reduction ratio is The largest when the encountering object is an object that partially obstructs the traffic flow, the next largest when the encountering object is an object that obstructs the traffic flow, and the encountering object is an object that temporarily blocks the lane. The smallest. As a result, the relationship between the distance from the risk and the difficulty level of lane change can be reflected in the value of the lane change risk potential for each risk classification based on the predicted risk potential.

Further, according to the vehicle traveling support method and the support device of the present embodiment, when the lane change risk potential is set to be smaller so as to move forward in the traveling direction from a position where the predicted risk potential is not 0, the reduction ratio is The largest when the object encountered is an object that temporarily blocks the lane, the next largest when the object encountered is an object that obstructs the traffic flow, and the object that is encountered is an object that partially obstructs the traffic flow. The smallest. As a result, the relationship between the distance from the risk and the difficulty level of lane change can be reflected in the value of the lane change risk potential for each risk classification based on the predicted risk potential.

Further, according to the vehicle traveling support method and the support device of the present embodiment, when the lane change risk potential is set to be smaller as the distance from the position where the predicted risk potential is not 0 is set, the ratio of the reduction is determined by the own vehicle V1. The higher the vehicle speed limit on the road on which the vehicle travels, the smaller the setting. As a result, the relationship between the vehicle speed limit and the braking distance of the vehicle required to avoid the risk can be reflected in the value of the lane change risk potential.

Further, according to the vehicle traveling support method and the support device of the present embodiment, when the lane change risk potential is set to be smaller as the distance from the position where the predicted risk potential is not 0 is set, the ratio of the reduction is the own vehicle V1. The higher the vehicle speed, the smaller the setting. As a result, the relationship between the vehicle speed of the own vehicle V1 and the braking distance of the vehicle required to avoid the risk can be reflected in the value of the lane change risk potential.

Further, according to the vehicle traveling support method and the support device of the present embodiment, when the encountering object is a congested lane waiting for a right turn or a left turn, which temporarily blocks the lane, the lane change risk potential is increased. Set higher as it gets closer to the right or left turn position. As a result, the relationship between the distance to the right or left turn position and the difficulty of changing lanes can be reflected in the value of the lane change risk potential.

Further, according to the vehicle traveling support method and the support device of the present embodiment, when the traveling of the own vehicle V1 is autonomously controlled, an object around the own vehicle V1 is detected, and when the object is detected, the object The actual risk potential of the vehicle is calculated, the integrated risk is calculated by integrating the actual risk potential, the predicted risk potential, and the lane change risk potential, and the lane with the smallest integrated risk is selected as the lane in which the own vehicle V1 travels. As a result, when the own vehicle V1 actually travels, it is possible to provide appropriate driving support by using the actual risk potential.

Further, according to the vehicle running support method and the support device of the present embodiment, when the running of the own vehicle V1 is autonomously controlled, an object around the own vehicle V1 is detected, and when the object is detected, the object By using the predicted driving behavior of other vehicles to avoid the risk due to the predicted risk potential, the actual risk potential, the predicted risk potential, and the lane are obtained. The change risk potential and the interrupt risk potential are integrated to calculate the integration risk, and the lane with the smallest integration risk is selected as the lane in which the own vehicle V1 travels. As a result, it is possible to set a traveling route using the interrupted risk potential in addition to the actual risk potential, and it is possible to provide appropriate driving support.

Further, according to the vehicle traveling support method and the support device of the present embodiment, the lane is located at the position where the lane change risk potential is the smallest between the current position P1 of the own vehicle V1 and the next right turn or left turn position. Make changes. As a result, the section for setting the lane change position can be divided into short sections, and the lane change position can be repeatedly examined in the short section. As a result, the appropriate position according to the surrounding driving conditions in real time. You will be able to change lanes with.

Further, according to the vehicle traveling support method and the support device of the present embodiment, when there are a plurality of routes from the current position P1 of the own vehicle V1 to the destination Px, the sum of the lane change risk potentials arranged on the routes. Selects the smallest route. As a result, by setting the route in advance, the position of changing lanes can be set in advance, and smooth running support can be provided.

Further, according to the vehicle traveling support method and the support device of the present embodiment, when it is determined that the own vehicle V1 travels on a road having three or more lanes and it is necessary to change lanes a plurality of times, the lane change is performed. The position where the sum of the risk potentials is the smallest is set as the position to change lanes. As a result, even when it is necessary to change lanes a plurality of times, it is possible to change lanes at a position where the risk associated with the lane change is small.

1000 ... Driving support system 1 ... Detection device 2 ... Navigation device 3 ... Map information 4 ... Own vehicle information detection device 5 ... Environment recognition device 6 ... Object recognition device 100 ... Driving support device 10 ... Processor 11 ... CPU
12 ... ROM
13 ... RAM
110 ... Output device 111 ... Communication device 120 ... Destination setting unit 130 ... Route planning unit 140 ... Operation planning unit 150 ... Travelable area calculation unit 160 ... Route calculation unit 1601 ... Path acquisition unit of peripheral objects 1602 ... Classification of peripheral objects Part 1603 ... Information storage part of peripheral objects 1604 ... Storage part 1605 ... Prediction risk map generation part 1606 ... Risk potential calculation part 1607 ... Encounter probability calculation part 1608 ... Prediction risk potential generation part 1609 ... Interrupted risk potential generation part 1610 ... Setting Route reading unit 1611 ... Lane change necessity determination unit 1612 ... Lane change risk determination unit 1613 ... Lane change risk potential generation unit 1614 ... Actual risk map learning unit 1615 ... Actual risk map generation unit 1616 ... Risk map integration unit 1617 ... Action decision Part 170 ... Driving behavior control unit 200 ... Vehicle control device 210 ... Drive mechanism 211 ... Communication device C ... Crossing D1 ... Road D11 ... Left lane D12 ... Central lane D13 ... Right lane D14 ... Right turn dedicated lane D2 ... Road D21 ... Left lane D22 ... Right lane D3 ... Side road D4 ... Road P1 ... Current position of own vehicle Px ... Destination R, R1, R2 of own vehicle ... Travel route Ra, R1a, R1b, R2a ... Travel route Rx, R1x, R1y ... Travel Routes S1, S2, S3, S4 ... Signals V1 ... Own vehicle V1a, V1b, V1c, V1d, V1e ... Positions where the own vehicle starts changing lanes V2a, V2b ... Other vehicles (while stopped)
V3a, V3b, V3c ... Other vehicles (turn left to the side road)
V4a, V4b, V4c, V4d, V4e, V4f ... Other vehicles (waiting for left turn)
V5a, V5b, V5c, V5d, V5e, V5f, V5g, V5h, V5i ... Other vehicles (waiting for right turn)
V2x, V3x, V4x, V5x ... Other vehicles (risk avoidance operation)

Claims (19)

When an object is detected in a vehicle, the risk potential of the object is calculated.
By associating the risk potential of the object with the encounter position where the object is encountered, the risk potential at the encounter position is accumulated as the actual risk potential.
Using the accumulated risk potential at the encounter position, the predicted risk potential of the object predicted to be encountered at the encounter position, which is lower than the risk potential obtained when the object is detected, is obtained.
When traveling again at the encounter position when traveling along the route to the destination, in the vehicle traveling support method for autonomously controlling the vehicle traveling using the predicted risk potential,
It is determined whether or not it is necessary to change the lane of the own vehicle in order to travel along the route.
When it is determined that the lane change is necessary, at least one of the actual risk potential and the predicted risk potential is used to calculate the lane change risk potential due to the risk associated with the driving operation of the lane change.
The risk potential of the own lane is obtained by adding the lane change risk potential to the predicted risk potential of the own lane in which the own vehicle travels.
A vehicle traveling support method for setting a position for changing lanes using the risk potential of the own lane. The method according to claim 1, wherein the lane change risk potential is arranged at a position adjacent to a position in the own lane where the predicted risk potential existing in the lane to be changed is not 0. The lane change risk potential is arranged at a position adjacent to a position in the own lane where the predicted risk potential existing in the lane to be changed is 0, and the lane change risk potential is a position where the predicted risk potential is not 0. The method according to claim 2, wherein the smaller the distance from the lane. The object according to any one of claims 1 to 3, wherein the object is an object that blocks the lane for a long time, an object that temporarily blocks the lane, an object that obstructs the traffic flow, or an object that partially obstructs the traffic flow. The method described. The lane change risk potential is greatest at a position adjacent to the predicted risk potential of an object that blocks the lane for a long time in the own lane, and is behind a position adjacent to the predicted risk potential of an object that blocks the lane for a long time. Position and the position adjacent to the predicted risk potential for the object that temporarily blocks the lane are the next largest, and the positions in front of and behind the position adjacent to the predicted risk potential for the object that temporarily blocks the lane are The smallest method according to claim 4. The claim that when the object is an object that blocks the lane for a long time, the lane change risk potential is arranged at a position adjacent to the predicted risk potential and a position behind the predicted risk potential in the own lane. The method according to 4. When the object is an object that temporarily blocks a lane, an object that obstructs a traffic flow, or an object that partially obstructs a traffic flow, the lane change risk potential is such that the predicted risk potential is 0 in the own lane. The method according to claim 4, wherein the positions adjacent to the non-positions and the positions in front of and / or behind the positions are arranged. The method according to claim 7, wherein the lane change risk potential arranged at the front and / or rear positions is set to be smaller as the distance from the position where the predicted risk potential is not 0 is set. When the lane change risk potential is set to be smaller so as to move backward from a position where the predicted risk potential is not 0 in the traveling direction, the reduction ratio is when the object partially obstructs the traffic flow. The method of claim 8, wherein the method is the largest, the second largest when the object obstructs a traffic flow, and the smallest when the object temporarily blocks a lane. When the lane change risk potential is set to be smaller so as to move forward in the traveling direction from a position where the predicted risk potential is not 0, the reduction ratio is the largest when the object temporarily blocks the lane. The method according to claim 8 or 9, wherein the method is the second largest when the object is an object that obstructs a traffic flow, and is the smallest when the object is an object that partially obstructs a traffic flow. 8. When the lane change risk potential is set to be smaller as the distance from the position where the predicted risk potential is not 0 is set, the ratio of the reduction is set to be smaller as the speed limit of the road on which the own vehicle travels is higher. The method according to any one of 10 to 10. When the lane change risk potential is set to be smaller as the distance from the position where the predicted risk potential is not 0 is set, the ratio of the reduction is set to be smaller as the vehicle speed of the own vehicle is higher, any of claims 8 to 11. The method described in paragraph 1. The lane change risk potential is set higher as the object is closer to the right or left turn position when the object is a congested lane waiting for a right turn or a left turn that temporarily blocks the lane, according to claims 4 to 12. The method according to any one item. When autonomously controlling the running of the own vehicle, the object around the own vehicle is detected.
When the object is detected, the actual risk potential of the object is obtained.
The integrated risk is calculated by integrating the actual risk potential, the predicted risk potential, and the lane change risk potential.
The method according to any one of claims 1 to 13, wherein the lane with the lowest integration risk is selected as the lane in which the own vehicle travels. When autonomously controlling the running of the own vehicle, the object around the own vehicle is detected.
When the object is detected, the actual risk potential of the object is obtained.
Using the predicted driving motion of another vehicle to avoid the risk due to the predicted risk potential, the interrupted risk potential lower than the predicted risk potential is obtained.
The integrated risk is calculated by integrating the actual risk potential, the predicted risk potential, the lane change risk potential, and the interrupt risk potential.
The method according to any one of claims 1 to 13, wherein the lane in which the own vehicle travels is selected so that the total risk of integration of the route to the destination is minimized. The lane change is performed at a position where the lane change risk potential is the smallest between the current position of the own vehicle and the next right turn or left turn position, according to any one of claims 1 to 15. Method. According to any one of claims 1 to 16, when there are a plurality of routes from the current position of the own vehicle to the destination, the route having the smallest sum of the lane change risk potentials arranged on the routes is selected. The method described. When it is determined that multiple lane changes are necessary when the own vehicle is traveling on a road with three or more lanes, the position where the sum of the lane change risk potentials is the smallest is set as the lane change position. Item 10. The method according to any one of Items 1 to 17. A detector for detecting objects around the vehicle,
A controller for obtaining the risk potential, predicted risk potential, and lane change risk potential of the object and autonomously controlling the running of the vehicle using the lane change risk potential.
A storage device for storing information on the object detected by the detector and information on the risk potential obtained by the controller is provided.
The controller
When the detector detects the object, the risk potential of the object is obtained.
The risk potential of the object is made to correspond to the encounter position where the object is encountered, and the risk potential at the encounter position is stored in the storage as the actual risk potential.
Using the actual risk potential at the encounter position accumulated in the storage, the predicted risk potential of the object predicted to be encountered at the encounter position, which is lower than the risk potential obtained when the object is detected, is determined. Ask,
In the case of traveling again at the encounter position when traveling along the route to the destination, in the vehicle traveling support device that autonomously controls the traveling of the vehicle using the predicted risk potential.
The controller
It is determined whether or not it is necessary to change the lane of the own vehicle in order to travel along the route.
When it is determined that the lane change is necessary, at least one of the actual risk potential and the predicted risk potential is used to calculate the lane change risk potential due to the risk associated with the driving operation of the lane change.
The risk potential of the own lane is obtained by adding the lane change risk potential to the predicted risk potential of the own lane in which the own vehicle travels.
A vehicle traveling support device that sets the position of the lane change using the risk potential of the own lane.

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