CN115339474A - Vehicle control method, device, electronic device, and self-driving vehicle - Google Patents

Abstract

The present disclosure provides a vehicle control method, device, electronic equipment and an automatic driving vehicle. It involves the field of artificial intelligence, especially the field of autonomous driving. The specific implementation scheme is: determining the target lane based on the environment information of the vehicle; generating virtual obstacles for the target lane; predicting the driving speed of the virtual obstacles; and controlling the driving speed of the vehicle in combination with the driving speed of the virtual obstacles. According to the solution of the present disclosure, the driving speed of the vehicle can be reasonably controlled, and the driving safety of the vehicle can be improved.

Description

Vehicle control method, device, electronic device, and self-driving vehicle

technical field

The present disclosure relates to the field of artificial intelligence, especially to the field of automatic driving.

Background technique

In the field of autonomous driving, due to the limited range of perception of autonomous vehicles, in some complex road conditions, there will be multiple blind spots that the vehicle cannot perceive, so that the vehicle cannot plan a reasonable driving speed based on the information it obtains. And there may be unpredictable obstacles in the blind zone, and a collision may occur. Therefore, how to increase the safety of vehicle driving has become an urgent technical problem to be solved.

Contents of the invention

The present disclosure provides a vehicle control method, device, electronic equipment and an automatic driving vehicle.

According to a first aspect of the present disclosure, there is provided a vehicle control method, comprising:

Determine the target lane based on the environmental information of the vehicle;

Generate virtual obstacles for the target lane;

Predict the driving speed of virtual obstacles;

Combined with the driving speed of the virtual obstacle, the driving speed of the vehicle is controlled.

According to a second aspect of the present disclosure, there is provided a vehicle control device, comprising:

A determining module, configured to determine the target lane based on the environment information of the vehicle;

A generation module is used to generate virtual obstacles for the target lane;

Prediction module, for predicting the traveling speed of virtual obstacle;

The control module is used to control the driving speed of the vehicle in combination with the driving speed of the virtual obstacle.

According to a third aspect of the present disclosure, an electronic device is provided, including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the method provided in the first aspect above.

According to a fourth aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute the method provided in the first aspect above.

According to a fifth aspect of the present disclosure, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the method provided in the first aspect above is implemented.

According to a sixth aspect of the present disclosure, a vehicle is provided, including the electronic device provided in the above third aspect.

According to a seventh aspect of the present disclosure, an automatic driving vehicle is provided, including the electronic device provided in the above third aspect.

According to the technical solution of the present disclosure, the driving speed of the vehicle can be reasonably controlled, and the driving safety of the vehicle can be improved.

The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present application will be readily apparent by reference to the drawings and the following detailed description.

Description of drawings

In the drawings, unless otherwise specified, the same reference numerals designate the same or similar parts or elements throughout the several drawings. The drawings are not necessarily drawn to scale. It should be understood that these drawings only depict some embodiments disclosed according to the application, and should not be regarded as limiting the scope of the application.

FIG. 1 is a first schematic diagram of a blind zone according to an embodiment of the present disclosure;

FIG. 2 is a schematic flowchart of a vehicle control method according to an embodiment of the present disclosure;

FIG. 3 is a first schematic diagram of an intersection scene according to an embodiment of the present disclosure;

FIG. 4 is a second schematic diagram of an intersection scene according to an embodiment of the present disclosure;

FIG. 5 is a second schematic diagram of a blind area according to an embodiment of the present disclosure;

Fig. 6 is a functional schematic diagram characterizing the processing behavior characteristics of an intersection where an accident occurs according to an embodiment of the present disclosure;

FIG. 7 is a schematic diagram of a vehicle control architecture according to an embodiment of the present disclosure;

FIG. 8 is a schematic structural diagram of a vehicle control device according to an embodiment of the present disclosure;

FIG. 9 is a schematic diagram of a scene of vehicle control according to an embodiment of the present disclosure;

FIG. 10 is a block diagram of an electronic device for implementing a vehicle control method of an embodiment of the present disclosure.

Detailed ways

Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and they should be regarded as exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

The terms "first", "second" and "third" in the description, embodiments and claims of the present disclosure and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence order. Furthermore, the terms "comprising" and "having", as well as any variations thereof, are intended to cover a non-exclusive inclusion, eg, a series of steps or elements. A method, system, product or device is not necessarily limited to those steps or elements explicitly listed, but may include other steps or elements not explicitly listed or inherent to the process, method, product or device.

Before introducing the technical solutions of the embodiments of the present disclosure, the technical terms that may be used in the present disclosure are further explained:

Main vehicle: self-driving vehicle.

Obstacle cars: Other vehicles that the self-driving vehicle encounters.

Blind area: Due to the occlusion of obstacles, the main vehicle cannot obtain the environmental information of the occlusion through the existing sensing technology. For the main vehicle, the occlusion area is a blind area.

High-precision map: used to describe the topological structure of the road, including the road connection relationship, the relative position of the road, the lane controlled by the traffic light, etc.

V2X: Vehicle to Everything, that is, information exchange between vehicles and the outside world. Enables communication between vehicles, vehicles and base stations, and base stations.

Human drivers will encounter many blind spots caused by obstacles during driving, so that human drivers cannot clearly know the traffic conditions in the blind spots. Therefore, when encountering such situations, human drivers will choose strategies such as slowing down and driving carefully. Similarly, during the driving process, the self-driving vehicle will also encounter blind spots formed by obstacles blocking the perception system of the main vehicle itself, and special methods are required to help the self-driving vehicles pass through the blind spots.

In related technologies, there are usually two ways to solve the blind spot problem: the first is to use V2X technology to eliminate the blind spot, and the other is to identify the blind spot through the strategy of the main vehicle and take corresponding actions.

Use V2X technology to eliminate blind spots: Since V2X communicates between vehicles, vehicles and base stations, and base stations, the problem of blind spots can be fundamentally eliminated, that is, blind spots no longer exist. Because when an obstacle blocks the perception system of the main vehicle itself, the main vehicle cannot perceive the traffic information in the area covered by the obstacle, but using V2X technology, if there are other obstacles in the blocked area, these obstacles will use V2X technology to send out The signal makes the main car aware of its existence, so the area covered by obstacles is not a blind spot for the main car, which fundamentally eliminates the problem of blind spots.

Identify the blind spot through the strategy of the main car and make corresponding actions: when the main car recognizes an obstacle, the position point of the main car and the width boundary point of the obstacle can form a triangle, and the location of the blind spot is the width boundary between the main car and the obstacle The extended area of the connection is shown in Figure 1. When the main vehicle recognizes the blind spot, it will make driving behaviors such as deceleration, lane change, and lateral deviation in the lane according to the location of the blind spot and the road environment.

Using the V2X method can completely eliminate the problem of blind spots, but the premise of using V2X technology is that V2X-related base stations are installed on the road and related equipment is installed on autonomous vehicles. Due to high cost and immature technology, roads and vehicles equipped with V2X technology are currently very limited, resulting in very limited application scenarios for using V2X to completely eliminate blind spots.

Identify blind spots through the strategy of the main car and make corresponding actions. When the main car is driving in a complex road scene, it will encounter a lot of obstacles, and each obstacle will block a certain area to form a blind spot. If each blind spot is If the cautious driving measures are taken, the driving passability of the main vehicle will be greatly reduced. At the same time, the classification of different blind spots is relatively complicated. It is difficult to distinguish which blind spots need to slow down, which need to change lanes, etc. It is easy to introduce some unnecessary lane changes, reduce driving rationality, and put autonomous vehicles in a more dangerous situation. surroundings.

In order to at least partly solve one or more of the above problems and other potential problems, the present disclosure proposes a vehicle control method, which can improve the rationality of the planned vehicle driving speed, thereby improving the driving safety of the vehicle.

An embodiment of the present disclosure provides a vehicle control method. FIG. 2 is a schematic flowchart of the vehicle control method according to the embodiment of the present disclosure. The vehicle control method can be applied to a vehicle control device. The vehicle control device is located in the electronic equipment, and the electronic equipment may be a part of the vehicle, or independent of the vehicle but capable of communicating with the vehicle. The electronic devices include but are not limited to stationary devices and/or mobile devices. For example, a fixed device includes but is not limited to a server, and the server may be a cloud server or a common server. For example, a mobile device includes, but is not limited to: one or more terminals in a mobile phone, a tablet computer, and a vehicle-mounted terminal. In some possible implementation manners, the vehicle control method may also be implemented by a processor invoking computer-readable instructions stored in a memory. As shown in Figure 2, the vehicle control method includes:

S201: Determine the target lane based on the environment information where the vehicle is located;

S202: Generate a virtual obstacle for the target lane;

S203: Predict the driving speed of the virtual obstacle;

S204: Control the driving speed of the vehicle in combination with the driving speed of the virtual obstacle.

In the embodiment of the present disclosure, the environment information includes road environment information. The road environment-related information includes, but is not limited to, information about the lane in which the vehicle is located, and information about other lanes that intersect with the lane in which the vehicle is located.

Wherein, the relevant information of the lane may include information such as the position, the permitted speed range of the lane, traffic lights, and whether there are obstacles.

In the embodiment of the present disclosure, the virtual obstacle is a virtual obstacle. The virtual obstacle can be a motor vehicle, a non-motor vehicle, or a robot. The above is only an exemplary description, and does not serve as a limitation on all possible types of virtual obstacles, but is not exhaustive here.

In the embodiment of the present disclosure, controlling the driving speed of the vehicle in combination with the driving speed of the virtual obstacle includes: adjusting the driving speed of the vehicle according to the planned route of the vehicle and in combination with the driving speed of the virtual obstacle.

According to the technical solution of the embodiment of the present disclosure, the target lane is determined based on the environment information of the vehicle; a virtual obstacle is generated for the target lane; the driving speed of the virtual obstacle is predicted; the driving speed of the vehicle is controlled based on the driving speed of the virtual obstacle; , during the driving process of the vehicle, the target road without obstacles is considered, and a virtual obstacle is generated for the target road, which improves the rationality of the determined driving speed of the vehicle, and can avoid the accident caused by the sudden appearance of obstacles on the target road. collision, thereby improving the safety of vehicle driving.

In some embodiments, S201 may include:

S201a: Determine a candidate lane based on the environment information of the vehicle;

S201b: In response to detecting that neither the candidate lane nor the upstream connecting lane of the candidate lane exists the first obstacle, determine the candidate lane as the target lane.

In the embodiments of the present disclosure, the number of candidate lanes is not limited. There can be one lane candidate, two lanes, or multiple lanes. The number of candidate lanes determined in S201a depends on the road environment information where the vehicle is located. The number of candidate lanes is related to real-time traffic information and basic road construction facilities.

Here, the basic road construction includes the number of lanes, lane road network, lane speed limit setting, traffic light duration setting, etc.

Here, the real-time road condition information includes at least the number of vehicles on the lane.

In the embodiment of the present disclosure, the first obstacle is an obstacle that needs to be considered by the vehicle when determining the passing route.

Here, the type of the first obstacle may be a motor vehicle or a non-motor vehicle.

In some embodiments, determining the candidate lane based on the environment information of the vehicle includes:

Check whether there is an intersection within a certain distance in front of the vehicle;

If there is an intersection, check whether the intersection has other lanes that are vertical and facing the vehicle lane and in front of the main vehicle, and use the detected other lanes as candidate lanes.

Here, a certain distance can be set or adjusted according to requirements. For example, set the value of a certain distance to 30 meters. For another example, set the fixed distance value to 50 meters.

Here, vertical and facing the vehicle lane and in front of the vehicle, the angle between the direction of the lane and the planned trajectory of the vehicle can be about 90° (for example, 90°±20°), the orientation of the lane is close to the planned trajectory of the vehicle, and the lane is located in front of the vehicle.

Figure 3 shows the first schematic diagram of the intersection scene, the planned trajectory of the vehicle (referred to as the main vehicle in Figure 3) is indicated by a slash, the smiling face indicates the candidate lane, and the cross indicates the non-candidate lane, as shown in Figure 3, in the current scene , there are two candidate lanes and four non-candidate lanes.

Figure 4 shows the second schematic diagram of the intersection scene, the planned trajectory of the vehicle (referred to as the main vehicle in Figure 4) is represented by a slash, and the smiling face represents the candidate lane, as shown in Figure 4, in the current scene, there is a candidate lane, Five non-candidate lanes.

In this way, the candidate lanes related to the vehicle can be determined, thereby providing an accurate calculation basis for selecting the target lane, and helping to improve the driving safety of the vehicle.

In some embodiments, S202 includes:

S202a: Determine a target blind area of the target lane, where the target blind area is a blind area caused by occlusion by a second obstacle, and the second obstacle is not an obstacle located on the target lane;

S202b: Generate a virtual obstacle for the target lane based on the target blind area.

In the embodiment of the present disclosure, the virtual obstacle is a virtual obstacle, not a real obstacle.

Here, the target blind area is a blind area caused by occlusion by the second obstacle. The second obstacle is an obstacle located on a non-target lane.

In this way, by generating a virtual obstacle for the target lane, the interference of the virtual obstacle to the vehicle is considered when determining the driving speed of the vehicle, which helps to improve the driving safety of the vehicle.

In some embodiments, S202a includes:

S202a1: Determine all blind spots on the target lane caused by occlusion by the second obstacle;

S202a2: Determine the size and position of each blind area;

S202a3: Based on the size and position of each blind area, determine a target blind area from all blind areas.

In this way, the screening accuracy of target blind spots can be improved, thereby helping to improve the safety of vehicle driving.

In some embodiments, determining the size of each dead zone includes:

In the case that overlapping or bordering blind areas corresponding to multiple second obstacles are detected, the overlapping or bordering blind areas are first merged, and then the size of the merged blind area is determined.

Here, the size of the blind area may be represented by one-dimensional information such as length, which may specifically be the length of a side of the blind area. The side length here refers to the maximum side length among the side lengths of the blind zone parallel to the target lane.

Here, the size of the blind zone can also be represented by two-dimensional information such as area.

For example, if the blind areas of several obstacles overlap and border each other, the blind areas of these obstacles are first merged into one large blind area, and then the subsequent calculation is performed.

Exemplarily, if the size of the dead zone is smaller than a certain threshold (for example, the length is less than 3m), the dead zone is regarded as an invalid blind zone and is no longer considered.

As shown in Figure 5, the blind area formed by the triangular obstacle is A1B1C1D1, which is recorded as No. 1 blind area. Since the blind areas of obstacle a and b have overlapping areas, the blind areas of obstacle a and b are combined to form a blind area A2B2C2D2, which is recorded as No. 2 blind area. The size of the blind area can be represented by one-dimensional information, that is, AB (A1B1 or A2B2) in FIG. 5 , or by two-dimensional information, that is, ABCD (A1B1C1D1 or A2B2C2D2) in FIG. 5 . Since the No. 1 blind spot is small, it is judged as an invalid blind spot, that is, there is no such blind spot, so now only the No. 2 blind spot is left, and the size of the No. 2 blind spot is calculated.

In this way, by properly merging target blind areas first, the screening speed of target blind areas can be improved.

In some embodiments, the target blind area is determined from all blind areas based on the size and position of each blind area, including:

A blind area whose vertical distance is smaller than a first preset threshold is determined as a target blind area, where the vertical distance is a vertical distance from a first projected point in the target blind area to the lane where the vehicle is located.

Here, the first preset threshold may be set or adjusted according to design requirements such as safety requirements or comfort requirements.

Continuing to take Figure 5 as an example, the blind area formed by the triangular obstacle is A1B1C1D1, which is recorded as No. 1 blind area. Since the blind areas of obstacle a and b have overlapping areas, the blind areas of obstacle a and b are combined to form a blind area A2B2C2D2, which is recorded as No. 2 blind area. Calculate the size of the No. 2 blind spot, and the vertical distance b_dist from the first projection point A2 closest to the main vehicle to the main vehicle track. If b_dist is less than the first preset threshold (for example, 20m), then the blind area is the target blind area, and virtual obstacles need to be generated in the blind area; No need to focus, no need to drive carefully.

In this way, determining the target blind area according to the vertical distance can improve the speed of determining the target blind area.

In some embodiments, determining the target blind area from all blind areas based on the size and position of each blind area also includes: there are two or more blind areas on the same target lane, and the size of the blind area is greater than the second preset threshold. In the case of , the blind spot closest to the main vehicle is determined as the target blind spot.

Here, the second preset threshold can be set or adjusted according to design requirements such as safety requirements or comfort requirements.

Continuing to take Figure 5 as an example, since the size of the No. 2 blind area is large enough, No. 2 blind area is an effective target blind area, so even if there may be No. 3 blind areas, No. 4 blind areas, etc. behind the No. 2 blind area, they are no longer calculated and considered.

In this way, the number of target blind spots to be considered can be reduced, which helps to improve the efficiency of speed planning for the vehicle.

In some embodiments, S202b includes:

S202b1: Determine the type of the virtual obstacle according to the height of the second obstacle;

S202b2: Determine the vertical point of the first projection point in the target blind area on the lane centerline of the lane where the target blind area is located as the position of the virtual obstacle;

S202b3: Determine the direction of the target lane as the speed direction of the virtual obstacle;

S202b4: Determine the recommended driving speed of the virtual obstacle according to the speed, position, and historical trajectory of the vehicle.

In the embodiments of the present disclosure, types of virtual obstacles include but are not limited to motor vehicles and non-motor vehicles.

In the embodiment of the present disclosure, the first projection point is the projection point closest to the lane where the vehicle is located that forms the blind spot of the target, such as A2 in FIG. 5 .

In the embodiment of the present disclosure, the position of the virtual obstacle may be set at the center position of the lane closest to the projection point of the blind area of the path of the main vehicle, such as position p in FIG. 5 .

The present disclosure does not limit the execution sequence of S202b1, S202b2 and S202b3. S202b1, S202b2, and S202b3 may be executed simultaneously, or any two steps may be executed first, and the other step may be executed later. In addition, it may also be executed in the order of S202b1, S202b2, and S202b3, or it may be executed in the order of S202b1, S202b3, and S202b2, or it may be executed in the order of S202b2, S202b1, and S202b3, or it may be executed in the order of S202b2, S202b3, and S202b1, It may also be executed in the order of S202b3, S202b1, and S202b2, or may be executed in the order of S202b3, S202b2, and S202b1.

In this way, the relevant information of the virtual obstacle can be accurately determined, which helps to improve the accuracy of the determined driving speed of the virtual obstacle, thereby helping to improve the driving safety of the vehicle.

In some embodiments, S202b4 includes:

S202b4a: Determine the virtual collision point and the distance from the vehicle to the virtual collision point, where the virtual collision point is the spatial collision point between the vehicle and the virtual obstacle;

S202b4b: Predict the time when the vehicle travels to the virtual collision point according to the distance from the vehicle to the virtual collision point and the speed of the vehicle;

S202b4c: Determine the collision speed according to the distance from the vehicle to the virtual collision point and the time the vehicle travels to the virtual collision point;

S202b4d: Determine the reasonable speed of the target lane according to the normal speed corresponding to the obstacle type, the recommended speed of the target lane, and the probability of an accident;

S202b4e: Determine the recommended driving speed of the virtual obstacle according to the collision speed and the reasonable speed of the target lane.

In order to determine the speed of the virtual obstacle, the identity of the vehicle (which can be called the main vehicle) and the virtual obstacle (which can be called the obstacle vehicle) can be exchanged, that is, the main vehicle is also an obstacle for the virtual obstacle vehicle, Knowing the speed, position, and historical trajectory of the main vehicle, the recommended driving speed of the virtual obstacle can be determined through the planning module. Assuming that the virtual obstacle is an intelligent body, it will take into account the behavior of the main vehicle to adjust its speed to avoid collisions during driving. The overall decision-making process is:

Calculate the time T _interact between the host vehicle and the collision point according to the formula (1). The collision point is the possible collision point between the host vehicle and the virtual obstacle in space, that is, O _i in FIG. 5 .

T _interact =c _dist /Vego (1)

Among them, c _dist is the distance between the main vehicle and the collision point, and _Vego is the speed of the main vehicle.

Calculate the collision velocity V _interact according to formula (2);

V _interact =b _dist /T _interact (2)

Among them, b _dist is the distance between the virtual obstacle and the collision point.

The reasonable speed V _normal of the target lane is determined according to formula (3).

V _normal =min(V _type ,V _road )*sigmoid(1/γ) (3)

Among them, V _type is the normal speed corresponding to the obstacle type, V _road is the recommended speed of the target lane, and γ is the probability of an accident at the intersection.

Specifically, sigmoid(z)=1/(1+e ^-z ), has a limited value in [0,1], and has the characteristics of changing smoothly as the value of z increases, as shown in Figure 6, which is in line with the treatment of human drivers Behavioral characteristics of intersections where accidents occur.

In this way, the recommended driving speed of the virtual obstacle can be determined, thereby providing a reference basis for subsequent control of the driving speed of the vehicle.

In some embodiments, S202b4e, comprising:

Under the condition that the collision speed is not greater than the reasonable speed of the target lane, the collision speed is determined as the recommended driving speed of the virtual obstacle;

When the collision speed is greater than the reasonable speed of the target lane, the recommended driving speed of the virtual obstacle is determined according to the collision speed, safety distance, and the maximum deceleration of the type of obstacle. The safety distance is the safety distance between the vehicle and the virtual obstacle. Distance, the maximum deceleration is the maximum deceleration corresponding to the type of virtual obstacle.

Judging whether the collision speed V _interact is reasonable according to the reasonable speed V _normal of the target lane, specifically:

If V _interact ≤ V _normal , the recommended driving speed of the virtual obstacle is V _obs = V _interact ;

If V _interact >V _normal , the virtual obstacle will decelerate to avoid collision with the main vehicle, and the recommended driving speed V _obs of the virtual obstacle is calculated according to formula (4).

V _obs ＝(s _dist +1/2*α _maxdecl *(T _interact ) ² )/T _interact (4)

Among them, s _dist is the safety distance, and α _maxdecl is the maximum deceleration of this type of obstacle vehicle.

In this way, the rationality of the recommended driving speed of the virtual obstacle can be improved.

In some embodiments, S202b1 includes: when the height of the second obstacle is not less than the third preset threshold, determining that the type of the virtual obstacle is a motor vehicle; when the height of the second obstacle is less than the third preset threshold In the case of the threshold value, it is determined that the type of the virtual obstacle is a non-motor vehicle.

For example, the type of the virtual obstacle is determined according to the maximum value of the height of the second obstacle causing the blind zone. If the maximum value of the height of the second obstacle is greater than the third preset threshold (such as 2m), then determine that the type of virtual obstacle is a car; if the maximum value of the height of the second obstacle is less than the third preset threshold (such as 2m ), then it is determined that the type of virtual obstacle is a non-motor vehicle.

In the embodiment of the present disclosure, the third preset threshold can be set or adjusted according to the real height of the object.

In this way, determining the type of the virtual obstacle through the height of the second obstacle can make the determined type of the virtual obstacle more accurate, thereby helping to improve the rationality of the recommended driving speed of the virtual obstacle, and further improving the driving safety of the vehicle. safety.

It should be understood that the schematic diagrams shown in Fig. 3, Fig. 4, Fig. 5 and Fig. 6 are only exemplary rather than limiting, and they are expandable. Various obvious changes and/or substitutions are made to the example in FIG. 6 , and the obtained technical solution still belongs to the disclosure scope of the embodiments of the present disclosure.

Fig. 7 is a schematic diagram of the architecture of vehicle control according to an embodiment of the present disclosure. As shown in Fig. 7, the architecture includes: a blind area identification module, a prediction module and a planning module, wherein the target blind area is screened by the blind area identification module, and according to the characteristics of the target blind area Generate specific virtual obstacles. The blind spot recognition module imports the virtual obstacles in the blind spot to the prediction module, and uses the existing prediction module to make predictions and generate prediction lines. The prediction module sends the blind spot virtual obstacles and their prediction lines into the planning module, and uses the existing planning module to calculate the driving behavior of the vehicle. The type, position, and speed of virtual obstacles can be adjusted according to the degree of influence of the blind spot on the main vehicle, and the prediction line generated by it will not cause the main vehicle to make unreasonable behaviors. In this way, the prediction module can be decoupled from the planning module and the blind area identification module, and the blind area identification module will not affect the performance of other modules. Neither the prediction module nor the planning module needs to be changed, and its mature and stable performance will not make the main vehicle behave unreasonably. Use the existing prediction module to predict the prediction line of the virtual obstacle, and use the existing planning module to formulate a reasonable driving behavior to avoid the virtual obstacle; in this way, the collision between the vehicle and the real obstacle in the target blind area can be avoided, and the driving performance can be improved. The rationality, thereby improving the safety of vehicle driving.

An embodiment of the present disclosure provides a vehicle control device. As shown in FIG. 8 , the vehicle control device may include: a determination module 801 for determining the target lane based on the environment information of the vehicle; a generation module 802 for The lane generates a virtual obstacle; the prediction module 803 is used to predict the driving speed of the virtual obstacle; the control module 804 is used to control the driving speed of the vehicle in combination with the driving speed of the virtual obstacle.

In some embodiments, the determining module 801 includes: a first determining submodule, configured to determine a candidate lane based on the environment information of the vehicle; a second determining submodule, configured to respond to the detection of the candidate lane and the upstream of the candidate lane There is no first obstacle on the connecting lanes, and the candidate lane is determined as the target lane.

In some embodiments, the generating module 802 includes: a third determining submodule, configured to determine a target blind area of the target lane, where the target blind area is a blind area caused by occlusion by a second obstacle, and the second obstacle is not located on the target lane Obstacles; generating sub-modules for generating virtual obstacles for target lanes based on target blind spots.

In some embodiments, the third determining submodule is used to: determine all blind spots on the target lane caused by second obstacle occlusion; determine the size and position of each blind spot; Determine the blind area of the target.

In some embodiments, the third determination submodule is configured to: in the case that overlapping or bordering blind areas corresponding to multiple second obstacles are detected, first merge the overlapping or bordering blind areas, and then determine The size of the merged dead zone.

In some embodiments, the third determining submodule is configured to: determine a blind spot whose vertical distance is less than a first preset threshold as a target blind spot, where the vertical distance is the distance between the first projected point in the target blind spot and the lane where the vehicle is located. vertical distance.

In some embodiments, the third determining submodule is used to: if there are two or more blind spots on the same target lane and satisfy the blind spot size is greater than the second preset threshold, the vehicle closest to the host vehicle The dead zone is determined as the upper target blind zone.

In some embodiments, the generating submodule is used to: determine the obstacle type of the virtual obstacle according to the height of the second obstacle; place the first projected point in the target blind spot on the center line of the lane where the target blind spot is located The vertical point is determined as the position of the virtual obstacle; the direction of the target lane is determined as the speed direction of the virtual obstacle; and the recommended driving speed of the virtual obstacle is determined according to the vehicle's speed, position, and historical trajectory.

In some embodiments, the generation submodule is used to: determine the virtual collision point and the distance from the vehicle to the virtual collision point, the virtual collision point is the collision point between the vehicle and the virtual obstacle in space; according to the distance between the vehicle and the virtual collision point The distance and the speed of the vehicle, predict the time when the vehicle travels to the virtual collision point; determine the collision speed according to the distance from the vehicle to the virtual collision point and the time when the vehicle travels to the virtual collision point; according to the normal speed corresponding to the obstacle type, the target lane The recommended speed and the probability of accidents determine the reasonable speed of the target lane; according to the collision speed and the reasonable speed of the target lane, the recommended driving speed of the virtual obstacle is determined.

In some embodiments, the generating submodule is used for: when the collision speed is not greater than the reasonable speed of the target lane, determine the collision speed as the recommended driving speed of the virtual obstacle; when the collision speed is greater than the reasonable speed of the target lane In the case of , according to the collision speed, safety distance, and the maximum deceleration of the type of obstacle, determine the recommended driving speed of the virtual obstacle. The safety distance is the safe distance between the vehicle and the virtual obstacle, and the maximum deceleration is the virtual obstacle The type corresponds to the maximum deceleration.

In some embodiments, the generating submodule is used to: determine that the type of the virtual obstacle is a motor vehicle when the height of the second obstacle is not less than a third preset threshold; In the case of the third preset threshold, it is determined that the type of the virtual obstacle is a non-motor vehicle.

Those skilled in the art should understand that the functions of each processing module in the vehicle control device of the embodiment of the present disclosure can be understood by referring to the relevant description of the aforementioned vehicle control method. The processing modules of the vehicle control device of the embodiment of the present disclosure can be understood through The functions described in the embodiments of the present disclosure may be realized by an analog circuit, and may also be realized by running software on an electronic device to perform the functions described in the embodiments of the present disclosure.

The vehicle control device of the disclosed embodiment can improve the safety of vehicle driving.

The embodiment of the present disclosure also provides a schematic diagram of a scene of vehicle control. As shown in FIG. 9 , the vehicle sends information related to the vehicle itself to an electronic device such as a cloud server; the information related to the vehicle itself includes status information of the vehicle, such as speed, location, etc.; According to the environment information of the vehicle, the electronic device determines the target lane for each vehicle, and generates virtual obstacles for the target lane; predicts the driving speed of the virtual obstacles; combines the driving speed of the virtual obstacles to determine the recommended driving speed of the vehicle. The electronic device returns the recommended driving speed of the vehicle to the vehicle, so that the vehicle can control the running of the vehicle according to the planned route at the recommended driving speed returned by the electronic device. In this way, the safety of driving the vehicle can be improved.

The present disclosure does not limit the number of vehicles and electronic devices, and practical applications may include multiple vehicles and multiple electronic devices.

It should be understood that the scene diagram shown in FIG. 9 is only schematic and non-restrictive. Those skilled in the art can make various obvious changes and/or replacements based on the example in FIG. 9 , and the obtained technical solutions still belong to the implementation of the present disclosure. Examples of public scope.

In the technical solution of the present disclosure, the acquisition, storage and application of the user's personal information involved are in compliance with relevant laws and regulations, and do not violate public order and good customs.

According to the embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, a computer program product, a vehicle, and an automatic driving vehicle.

FIG. 10 shows a schematic block diagram of an example electronic device 1000 that may be used to implement embodiments of the present disclosure. Electronic device is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are by way of example only, and are not intended to limit implementations of the disclosure described and/or claimed herein.

As shown in FIG. 10, the device 1000 includes a computing unit 1001, which can be loaded into a random access memory (Random Access Memory, RAM) according to a computer program stored in a read-only memory (Read-Only Memory, ROM) 1002 or from a storage unit 1008. ) 1003 to execute various appropriate actions and processes. In the RAM 1003, various programs and data necessary for the operation of the device 1000 can also be stored. The computing unit 1001 , ROM 1002 , and RAM 1003 are connected to each other through a bus 1004 . An input/output (Input/Output, I/O) interface 1005 is also connected to the bus 1004 .

Multiple components in the device 1000 are connected to the I/O interface 1005, including: an input unit 1006, such as a keyboard, a mouse, etc.; an output unit 1007, such as various types of displays, speakers, etc.; a storage unit 1008, such as a magnetic disk, an optical disk, etc. ; and a communication unit 1009, such as a network card, a modem, a wireless communication transceiver, and the like. The communication unit 1009 allows the device 1000 to exchange information/data with other devices through a computer network such as the Internet and/or various telecommunication networks.

The computing unit 1001 may be various general-purpose and/or special-purpose processing components having processing and computing capabilities. Some examples of the computing unit 1001 include, but are not limited to, a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU), various dedicated artificial intelligence (Artificial Intelligence, AI) computing chips, various operating machines A computing unit of a learning model algorithm, a digital signal processor (Digital Signal Processor, DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 1001 executes various methods and processes described above, such as a vehicle control method. For example, in some embodiments, the vehicle control method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 1008 . In some embodiments, part or all of the computer program may be loaded and/or installed on the device 1000 via the ROM 1002 and/or the communication unit 1009 . When the computer program is loaded into the RAM 1003 and executed by the computing unit 1001, one or more steps of the vehicle control method described above may be performed. Alternatively, in other embodiments, the computing unit 1001 can be configured to execute the vehicle control method in any other suitable way (for example, by means of firmware).

Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (Field Programmable Gate Array, FPGA), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), Application-Specific Standard Products (ASSP), System on Chip (SOC), Complex Programmable Logic Device (CPLD), computer hardware, firmware, software, and/or realized in combination of them. These various embodiments may include being implemented in one or more computer programs executable and/or interpreted on a programmable system including at least one programmable processor, the programmable processor Can be special-purpose or general-purpose programmable processor, can receive data and instruction from storage system, at least one input device, and at least one output device, and transmit data and instruction to this storage system, this at least one input device, and this at least one output device an output device.

Program codes for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special purpose computer, or other programmable data processing devices, so that the program codes, when executed by the processor or controller, make the functions/functions specified in the flow diagrams and/or block diagrams Action is implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media would include one or more wire-based electrical connections, portable computer disks, hard disks, random access memory, read-only memory, Erasable Programmable Read-Only Memory , EPROM), flash memory, optical fiber, portable compact disk read only memory (Compact Disk Read Only Memory, CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.

To provide for interaction with a user, the systems and techniques described herein can be implemented on a computer having a display device (e.g., a cathode ray tube (CRT) or a liquid crystal display (LCD)) for displaying information to the user. Liquid Crystal Display (LCD) monitor); and a keyboard and pointing device (eg, a mouse or trackball) through which the user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and can be in any form (including Acoustic input, speech input, or tactile input) to receive input from the user.

The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., as a a user computer having a graphical user interface or web browser through which a user can interact with embodiments of the systems and techniques described herein), or including such backend components, middleware components, Or any combination of front-end components in a computing system. The components of the system can be interconnected by any form or medium of digital data communication, eg, a communication network. Examples of communication networks include: a local area network (Local Area Network, LAN), a wide area network (Wide Area Network, WAN), and the Internet.

A computer system may include clients and servers. Clients and servers are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by computer programs running on the respective computers and having a client and server relationship to each other. The server can be a cloud server, a server of a distributed system, or a server combined with a blockchain.

It should be understood that steps may be reordered, added or deleted using the various forms of flow shown above. For example, each step described in the present disclosure may be executed in parallel, sequentially, or in a different order, as long as the desired result of the technical solution disclosed in the present disclosure can be achieved, no limitation is imposed herein.

The specific implementation manners described above do not limit the protection scope of the present disclosure. It should be apparent to those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made depending on design requirements and other factors. Any modifications, equivalent replacements and improvements made within the principles of the present disclosure shall be included within the protection scope of the present disclosure.

Claims (27)

1. A vehicle control method comprising:

determining a target lane based on the environmental information of the vehicle;

generating a virtual obstacle for the target lane;

predicting a travel speed of the virtual obstacle;

and controlling the running speed of the vehicle by combining the running speed of the virtual obstacle.

2. The method of claim 1, wherein the determining a target lane based on environmental information in which the vehicle is located comprises:

determining candidate lanes based on the environmental information of the vehicle;

determining the candidate lane as the target lane in response to detecting that neither the candidate lane nor an upstream connecting lane of the candidate lane has a first obstacle.

3. The method of claim 1, wherein the generating a virtual obstacle for the target lane comprises:

determining a target blind area of the target lane, wherein the target blind area is a blind area caused by shielding of a second obstacle, and the second obstacle is not an obstacle located on the target lane;

generating the virtual obstacle for the target lane based on the target blind area.

4. The method of claim 3, wherein the determining a target blind spot of the target lane comprises:

determining all blind areas on the target lane, which are caused by the shielding of the second barrier;

determining the size and the position of each blind area;

and determining the target blind area from all the blind areas based on the size and the position of each blind area.

5. The method of claim 4, wherein determining the size of each blind zone comprises:

and under the condition that the overlapping or the bordering of the blind areas corresponding to the plurality of second obstacles is detected, combining the overlapped or bordered blind areas, and then determining the size of the combined blind areas.

6. The method of claim 4, wherein the determining the target blind zone from all blind zones based on the size and the position of each blind zone comprises:

and determining a blind area with a vertical distance smaller than a first preset threshold value as the target blind area, wherein the vertical distance is the vertical distance from a first projection point in the target blind area to a lane where the vehicle is located.

7. The method of claim 6, wherein the determining the target blind zone from all blind zones based on the size and location of each blind zone further comprises:

and determining the blind area closest to the main vehicle as the target blind area under the condition that two or more blind areas exist on the same target lane and the size of the blind area is larger than a second preset threshold value.

8. The method of claim 3, wherein the generating the virtual obstacle for the target lane based on the target blind spot comprises:

determining the obstacle type of the virtual obstacle according to the height of the second obstacle;

determining a vertical point of a first projection point in the target blind area on a lane center line of a lane where the target blind area is located as the position of the virtual obstacle;

determining a direction of the target lane as a speed direction of the virtual obstacle;

and determining the recommended running speed of the virtual obstacle according to the speed, the position and the historical track of the vehicle.

9. The method of claim 8, wherein said determining a recommended travel speed of the virtual obstacle based on the speed, position, historical trajectory of the vehicle comprises:

determining a virtual collision point and a distance from the vehicle to the virtual collision point, wherein the virtual collision point is a collision point of the vehicle and the virtual obstacle on the space;

predicting the time for the vehicle to travel to the virtual collision point according to the distance from the vehicle to the virtual collision point and the speed of the vehicle;

determining a collision speed according to the distance from the vehicle to the virtual collision point and the time for the vehicle to travel to the virtual collision point;

determining the reasonable speed of the target lane according to the normal speed corresponding to the type of the obstacle, the recommended speed of the target lane and the probability of accidents;

and determining the recommended driving speed of the virtual obstacle according to the collision speed and the reasonable speed of the target lane.

10. The method of claim 9, wherein determining the recommended travel speed of the virtual obstacle based on the collision speed and the reasonable speed of the target lane comprises:

determining the collision speed as a recommended travel speed of the virtual obstacle if the collision speed is not greater than a reasonable speed of the target lane;

and under the condition that the collision speed is greater than the reasonable speed of the target lane, determining the recommended running speed of the virtual obstacle according to the collision speed, a safe distance and the maximum deceleration of the type of the obstacle, wherein the safe distance is the safe distance between the vehicle and the virtual obstacle, and the maximum deceleration is the maximum deceleration corresponding to the type of the virtual obstacle.

11. The method of claim 8, wherein said determining a type of the virtual obstacle from the height of the second obstacle comprises:

determining the type of the virtual obstacle to be a motor vehicle under the condition that the height of the second obstacle is not less than a third preset threshold value;

determining that the type of the virtual obstacle is a non-motor vehicle if the height of the second obstacle is less than the third preset threshold.

12. A vehicle control apparatus comprising:

the determining module is used for determining a target lane based on the environmental information of the vehicle;

a generating module for generating a virtual obstacle for the target lane;

a prediction module for predicting a travel speed of the virtual obstacle;

and the control module is used for controlling the running speed of the vehicle by combining the running speed of the virtual obstacle.

13. The apparatus of claim 12, wherein the means for determining comprises:

a first determination submodule for determining a candidate lane based on environmental information in which the vehicle is located;

a second determination submodule configured to determine the candidate lane as the target lane in response to detecting that neither the candidate lane nor an upstream connecting lane of the candidate lane has a first obstacle.

14. The apparatus of claim 12, wherein the generating means comprises:

a third determining submodule, configured to determine a target blind area of the target lane, where the target blind area is a blind area caused by shielding of a second obstacle, and the second obstacle is not an obstacle located on the target lane;

a generating submodule for generating the virtual obstacle for the target lane based on the target blind area.

15. The apparatus of claim 14, wherein the third determination submodule is to:

determining all blind areas on the target lane, which are caused by the shielding of the second obstacle;

determining the size and the position of each blind area;

and determining the target blind area from all the blind areas based on the size and the position of each blind area.

16. The apparatus of claim 15, wherein the third determination submodule is to:

and under the condition that the overlapping or the bordering of the blind areas corresponding to the plurality of second obstacles is detected, combining the overlapped or bordered blind areas, and then determining the size of the combined blind areas.

17. The apparatus of claim 15, wherein the third determination submodule is to:

and determining a blind area with a vertical distance smaller than a first preset threshold value as the target blind area, wherein the vertical distance is the vertical distance from a first projection point in the target blind area to a lane where the vehicle is located.

18. The apparatus of claim 17, wherein the third determination submodule is to:

and under the condition that two or more blind areas exist on the same target lane and the size of the blind area is larger than a second preset threshold value, determining the blind area closest to the main vehicle as the target blind area.

19. The apparatus of claim 14, wherein the generation submodule is to:

determining the obstacle type of the virtual obstacle according to the height of the second obstacle;

determining a vertical point of a first projection point in the target blind area on a lane center line of a lane where the target blind area is located as the position of the virtual obstacle;

determining a direction of the target lane as a speed direction of the virtual obstacle;

and determining the recommended running speed of the virtual obstacle according to the speed, the position and the historical track of the vehicle.

20. The apparatus of claim 19, wherein the generation submodule is to:

determining a virtual collision point and a distance from the vehicle to the virtual collision point, wherein the virtual collision point is a collision point of the vehicle and the virtual obstacle on the space;

predicting the time for the vehicle to travel to the virtual collision point according to the distance from the vehicle to the virtual collision point and the speed of the vehicle;

determining a collision speed according to the distance from the vehicle to the virtual collision point and the time for the vehicle to travel to the virtual collision point;

determining the reasonable speed of the target lane according to the normal speed corresponding to the type of the obstacle, the recommended speed of the target lane and the probability of accidents;

and determining the recommended driving speed of the virtual obstacle according to the collision speed and the reasonable speed of the target lane.

21. The apparatus of claim 20, wherein the generation submodule is to:

determining the collision speed as a recommended travel speed of the virtual obstacle if the collision speed is not greater than a reasonable speed of the target lane;

and under the condition that the collision speed is greater than the reasonable speed of the target lane, determining the recommended running speed of the virtual obstacle according to the collision speed, a safe distance and the maximum deceleration of the type of the obstacle, wherein the safe distance is the safe distance between the vehicle and the virtual obstacle, and the maximum deceleration is the maximum deceleration corresponding to the type of the virtual obstacle.

22. The apparatus of claim 19, wherein the generation submodule is to:

determining that the type of the virtual obstacle is a motor vehicle under the condition that the height of the second obstacle is not less than a third preset threshold value;

determining that the type of the virtual obstacle is a non-motor vehicle if the height of the second obstacle is less than the third preset threshold.

23. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-11.

24. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-11.

25. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-11.

26. A vehicle, comprising: comprising an electronic device as claimed in claim 23.

27. An autonomous vehicle comprising: comprising an electronic device as claimed in claim 23.

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