CN115657684B - Method, device, device, and computer-readable medium for generating vehicle route information - Google Patents

Abstract

Embodiments of the present disclosure disclose a vehicle route information generating method, device, device and computer-readable medium. A specific embodiment of the method includes: acquiring the current vehicle perception information, lane line perception information, the first obstacle feature information set, the second obstacle feature information set, and the lane path information sequence; for the first obstacle feature information set and The second obstacle feature information set is fused to obtain the current lane obstacle feature information set and the left lane obstacle feature information set; based on the current vehicle perception information and the current lane obstacle feature information set, lane change expectation information is generated; response To determine that the lane change expectation information satisfies the preset expected lane change conditions, generate lane merging information; detect and process the obstacle feature information group on the left lane, and obtain lane change decision information; generate lane change trajectory information; generate vehicle path information and send To the vehicle control module to control the driving of the vehicle. This embodiment can shorten the driving time of the vehicle.

Description

Method, device, device, and computer-readable medium for generating vehicle route information

technical field

The embodiments of the present disclosure relate to the field of computer technology, and in particular to a method, device, device, and computer-readable medium for generating vehicle route information.

Background technique

A method for generating vehicle route information is an important technology in the field of intelligent driving. At present, when generating vehicle route information, the usual method is: the vehicle navigation device plans and outputs the vehicle route information composed of each lane path according to the current position and destination of the vehicle, so as to control the vehicle to drive safely along the planned route to the destination.

However, the inventors have found that when the vehicle route information is generated in the above manner, the following technical problems often exist:

First, although the vehicle can drive to the destination along the planned route, the speed of the vehicle has to be lower than the expected speed in order to maintain a safe distance during the long-term follow-up process, resulting in a longer driving time and reducing the driving efficiency of the vehicle. efficiency;

Second, if the vehicle changes lanes in order to increase the speed, it usually needs to rely on the car navigation to regenerate the vehicle route information according to the vehicle location and destination, resulting in a large occupation of computing resources;

Third, in the process of changing lanes, there may be unexpected situations such as the sudden deceleration of the vehicle in front of the target lane or the sudden acceleration of the vehicle behind the target lane. A collision will reduce the safety of the vehicle.

The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art.

Contents of the invention

The Summary of the Disclosure is provided to present concepts in a simplified form that are described in detail in the Detailed Description that follows. The content of this disclosure is not intended to identify the key features or essential features of the claimed technical solution, nor is it intended to be used to limit the scope of the claimed technical solution.

Some embodiments of the present disclosure provide a method, device, device and computer-readable medium for generating vehicle route information to solve one or more of the technical problems mentioned in the background art section above.

In the first aspect, some embodiments of the present disclosure provide a method for generating vehicle route information, the method including: acquiring current vehicle perception information, lane line perception information, a first set of obstacle feature information, and a second set of obstacle feature information and the lane path information sequence; based on the above-mentioned lane line perception information, the above-mentioned first obstacle feature information set and the above-mentioned second obstacle feature information set are fused to obtain the current lane obstacle feature information group and the left lane obstacle feature information group; based on the above-mentioned current vehicle perception information and the above-mentioned current lane obstacle feature information group, generate lane-changing expected information; in response to determining that the above-mentioned lane-changing expectation information meets preset expected lane-changing conditions, path information sequence, generating lane merging information; in response to determining that the above lane changing information satisfies the preset distance condition, detecting and processing the above left lane obstacle feature information group to obtain lane change decision information; in response to determining the above lane change decision information Satisfy the preset lane changing conditions, generate lane changing trajectory information based on the preset high-precision map, the above-mentioned current vehicle perception information, the above-mentioned left lane obstacle feature information group, and the above-mentioned lane line perception information; based on the above-mentioned lane changing trajectory information, The above-mentioned merging information and the above-mentioned lane path information sequence generate vehicle path information, and send the above-mentioned vehicle path information to the vehicle control module for controlling vehicle driving, wherein the above-mentioned generation of vehicle path information includes: based on the above-mentioned lane change trajectory information and the above merging information to generate merging trajectory information; based on the above merging information, intercept the above lane path information sequence to obtain the intercepted lane path information sequence; combine the above lane changing trajectory information, the above merging trajectory information and the above intercepted rear lane The route information sequence is determined as vehicle route information.

In the second aspect, some embodiments of the present disclosure provide a device for generating vehicle route information, the device includes: an acquisition unit configured to acquire current vehicle perception information, lane line perception information, the first obstacle feature information set, the second The obstacle feature information set and the lane path information sequence; the fusion processing unit is configured to perform fusion processing on the first obstacle feature information set and the second obstacle feature information set based on the lane line perception information to obtain the current lane The obstacle characteristic information group and the left lane obstacle characteristic information group; the first generation unit is configured to generate lane change expectation information based on the above-mentioned current vehicle perception information and the above-mentioned current lane obstacle characteristic information group; the second generation unit, configured to generate lane merging information based on the current vehicle perception information and the lane path information sequence in response to determining that the lane change expectation information satisfies preset expected lane change conditions; the detection processing unit is configured to respond to determining the lane merge The information satisfies the preset distance condition, and the above-mentioned left lane obstacle feature information group is detected and processed to obtain the lane-changing decision information; the third generating unit is configured to respond to determining that the above-mentioned lane-changing decision information meets the preset lane-changing condition, Based on the preset high-precision map, the above-mentioned current vehicle perception information, the above-mentioned left lane obstacle characteristic information group and the above-mentioned lane line perception information, generate lane-changing trajectory information; the generating and sending unit is configured to be based on the above-mentioned lane-changing trajectory information , the above-mentioned merging information and the above-mentioned lane path information sequence, generate vehicle path information, and send the above-mentioned vehicle path information to the vehicle control module for controlling vehicle driving, wherein the above-mentioned generation of vehicle path information includes: based on the above-mentioned lane change trajectory information and The above merging information generates merging trajectory information; based on the above merging information, the above lane path information sequence is intercepted to obtain the intercepted lane path information sequence; the above lane changing trajectory information, the above merging trajectory information and the above intercepted The sequence of lane route information is determined as vehicle route information.

In a third aspect, some embodiments of the present disclosure provide an electronic device, including: one or more processors; The processor executes, so that one or more processors implement the method described in any implementation manner of the first aspect above.

In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium on which a computer program is stored, wherein, when the computer program is executed by a processor, the method described in any implementation manner of the above-mentioned first aspect is implemented.

The above-mentioned embodiments of the present disclosure have the following beneficial effects: through the method for generating vehicle route information in some embodiments of the present disclosure, the time-consuming vehicle driving can be shortened. Specifically, the reason why the vehicle takes a long time to drive is that although the vehicle can drive to the destination along the planned route, the vehicle has to be slower than the expected speed in order to maintain a safe distance during the long-term follow-up process. Based on this, the method for generating vehicle route information in some embodiments of the present disclosure firstly acquires current vehicle perception information, lane line perception information, first obstacle feature information set, second obstacle feature information set, and lane path information sequence. Here, information about the current vehicle, the lane line on the road, and the obstacle vehicle can be obtained, so as to facilitate the subsequent generation of lane change decision information based on the above information to determine whether the current vehicle needs to change lanes to the vehicle on the left for acceleration. Secondly, based on the lane line perception information, the first obstacle feature information set and the second obstacle feature information set are fused to obtain the current lane obstacle feature information set and the left lane obstacle feature information set. In this way, it is convenient to subsequently generate lane change expectation information based on information of obstacle vehicles on the current lane, and generate lane change decision information based on information of obstacle vehicles on the left lane. Then, based on the above-mentioned current vehicle perception information and the above-mentioned current lane obstacle feature information group, lane change expectation information is generated. In response to determining that the lane change expectation information satisfies a preset expected lane change condition, lane merging information is generated based on the current vehicle perception information and the lane path information sequence. In response to determining that the merging information satisfies the preset distance condition, the above-mentioned left lane obstacle feature information group is detected and processed to obtain lane-changing decision information. Thus, it can be determined according to the lane-changing expectation information of the current vehicle whether the current vehicle needs to change lanes to the vehicle on the left for acceleration. Considering that the current vehicle needs to continue to drive closer to the destination, the merge information is further generated on the basis of the lane change expectation information, and then the lane change decision information is finally determined according to the information of the obstacle vehicle on the left lane , in order to determine whether to generate a lane-changing trajectory for the current vehicle according to the lane-changing decision information for the vehicle to change lanes to the left fast lane, so as to increase the speed of the vehicle and shorten the time spent on driving. Afterwards, in response to determining that the lane change decision information meets the preset lane change conditions, a lane change is generated based on the preset high-precision map, the current vehicle perception information, the left lane obstacle characteristic information group, and the lane line perception information. track information. Thus, it can be determined through the lane change decision information that the current vehicle will change lanes to the left lane, and based on this, generate lane change trajectory information for the current vehicle to perform a lane change operation. Finally, based on the above-mentioned lane change trajectory information, the above-mentioned lane-merging information and the above-mentioned lane path information sequence, vehicle path information is generated, and the above-mentioned vehicle path information is sent to the vehicle control module for controlling the driving of the vehicle. Wherein, the above-mentioned generation of vehicle path information includes: generating merging trajectory information based on the above-mentioned lane-changing trajectory information and the above-mentioned merging information; based on the above-mentioned merging information, intercepting the above-mentioned lane path information sequence to obtain the intercepted lane path information sequence; The above-mentioned lane change trajectory information, the above-mentioned lane-merging trajectory information and the above-mentioned intercepted lane path information sequence are determined as vehicle path information. In this way, the vehicle route information can be generated for the vehicle to maintain a relatively high speed, thereby shortening the driving time. Therefore, the method for generating vehicle route information of the present disclosure can enable the vehicle to increase the driving speed by changing lanes to the fast lane on the premise of maintaining a safe distance from the preceding vehicle. And because the vehicles after changing lanes still need to drive towards the direction close to the destination, the consideration of the merging information is added when determining the lane changing decision information, and the merging information is also considered when generating the vehicle route information. Make sure that the vehicle follows the shortest route planned when the vehicle is at the departure point as far as possible. Therefore, the time-consuming driving of the vehicle can be shortened. Furthermore, the efficiency of vehicle running can be improved.

Description of drawings

The above and other features, advantages and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic and elements and elements have not necessarily been drawn to scale.

FIG. 1 is a flowchart of some embodiments of a method for generating vehicle route information according to the present disclosure;

Fig. 2 is a structural schematic diagram of some embodiments of a device for generating vehicle route information according to the present disclosure;

FIG. 3 is a schematic structural diagram of an electronic device suitable for implementing some embodiments of the present disclosure.

Detailed ways

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the disclosure are shown in the drawings, it should be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these examples are provided so that the understanding of this disclosure will be thorough and complete. It should be understood that the drawings and embodiments of the present disclosure are for exemplary purposes only, and are not intended to limit the protection scope of the present disclosure.

It should also be noted that, for the convenience of description, only the parts related to the related invention are shown in the drawings. In the case of no conflict, the embodiments in the present disclosure and the features in the embodiments can be combined with each other.

It should be noted that concepts such as "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the sequence of functions performed by these devices, modules or units or interdependence.

It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative and not restrictive, and those skilled in the art should understand that unless the context clearly indicates otherwise, it should be understood as "one or more" multiple".

The names of messages or information exchanged between multiple devices in the embodiments of the present disclosure are used for illustrative purposes only, and are not used to limit the scope of these messages or information.

The present disclosure will be described in detail below with reference to the accompanying drawings and embodiments.

FIG. 1 shows a flow 100 of some embodiments of a method for generating vehicle route information according to the present disclosure. The method for generating vehicle route information includes the following steps:

Step 101 , acquiring current vehicle perception information, lane line perception information, a first set of obstacle feature information, a second set of obstacle feature information, and a sequence of lane path information.

In some embodiments, the execution subject of the method for generating vehicle route information (such as a vehicle-mounted terminal) can obtain the current vehicle perception information, lane line perception information, first obstacle feature information set, second obstacle Object feature information set and lane path information sequence. Wherein, the above-mentioned current vehicle perception information may include vehicle speed value and current vehicle coordinates. The aforementioned vehicle speed value may be the current vehicle speed value output by the inertial navigation device. The aforementioned current vehicle coordinates may be GPS (Global Positioning System, Global Positioning System) positioning coordinates of the current vehicle output by the vehicle navigation device. The above-mentioned lane line perception information may be information about lane lines on both sides of the lane where the current vehicle is located outputted by the smart camera. The above lane line perception information may include left lane line information and right lane line information. The left lane marking information may include a curve of the left lane marking and a distance value of the left lane marking. The above right lane marking information may include the right lane marking curve and the right lane distance value. The above left lane line curve may represent the left lane line of the lane where the current vehicle is located. The above-mentioned left lane distance value may be the distance value from the center point of the current front axle of the vehicle to the left lane line. The above right lane line curve may represent the right lane line of the lane where the current vehicle is located. The above-mentioned right lane distance value may be the distance value from the center point of the current front axle of the vehicle to the right lane line. The first obstacle feature information in the first obstacle feature information set may be information about obstacle vehicles output by the smart camera. The aforementioned obstacle vehicle may be a vehicle-type obstacle. The first obstacle characteristic information in the first obstacle characteristic information set may include but not limited to at least one of the following: first azimuth angle information, a first lateral distance value, and a first longitudinal distance value. The above-mentioned first azimuth angle information may include an azimuth identifier and an angle value. The above orientation identification can uniquely identify the orientation of the obstacle vehicle relative to the current vehicle. For example, the above orientation identification may be "left front". The above-mentioned first lateral distance value may be a lateral distance value between the obstacle vehicle and the current vehicle. The above-mentioned first longitudinal distance value may be a longitudinal distance value between the obstacle vehicle and the current vehicle. The above-mentioned second obstacle feature information set may be the information of the obstacle vehicle output by the millimeter wave radar. The second obstacle characteristic information in the second obstacle characteristic information set may include but not limited to at least one of the following: second azimuth angle information, a second lateral distance value, and a second longitudinal distance value. The above-mentioned second azimuth angle information may be information about the azimuth and included angle of the obstacle vehicle from the current driving direction of the vehicle. The above-mentioned second azimuth angle information may include an azimuth identifier and an angle value. The above-mentioned second lateral distance value may be a lateral distance value between the obstacle vehicle and the current vehicle. The above-mentioned second longitudinal distance value may be a longitudinal distance value between the obstacle vehicle and the current vehicle. The lane path information in the above lane path information sequence may be information about the lane and the path on the lane of the current vehicle traveling from the starting point to the end point planned and output by the vehicle navigation device. The lane path information in the above lane path information sequence may include lane signs, road signs, adjacent lane information groups, lane start coordinates, lane path trajectory curves, and path length values. The above-mentioned lane identification can uniquely identify the lane. The above road identification can uniquely identify the road where the lane is located. The adjacent lane information in the above adjacent lane information group may include left adjacent lane identifiers and right adjacent lane identifiers. The left adjacent lane mark may be the mark of the adjacent lane on the left side of the corresponding lane. The right adjacent lane mark may be the mark of the adjacent lane on the right side of the corresponding lane. The lanes in the aforementioned lane group may be lanes arranged in parallel with the same direction. The aforementioned starting coordinates of the lane may be the coordinates of the starting point on the centerline of the lane. The lane path trajectory curves described above can be used to characterize the path on the corresponding lane. The aforementioned path length value may be a length value of the path on the corresponding lane.

Step 102 , based on the lane line perception information, perform fusion processing on the first obstacle feature information set and the second obstacle feature information set to obtain the current lane obstacle feature information set and the left lane obstacle feature information set.

In some embodiments, the execution subject may perform fusion processing on the first obstacle feature information set and the second obstacle feature information set in various ways based on the lane line perception information to obtain the current lane obstacle feature information group and left lane obstacle characteristic information group. Wherein, the current lane obstacle feature information in the current lane obstacle feature information group may be used to characterize the obstacle vehicle on the current lane. The current lane may be the lane where the current vehicle is located. The left lane obstacle characteristic information in the left lane obstacle characteristic information group can be used to characterize the obstacle vehicle on the left lane. The above-mentioned left lane may be the left adjacent lane of the lane where the current vehicle is located.

In some optional implementations of some embodiments, the execution subject may perform fusion processing on the first obstacle feature information set and the second obstacle feature information set based on the lane line perception information through the following steps, Obtain the current lane obstacle characteristic information group and the left lane obstacle characteristic information group:

The first step is to perform deduplication processing on each first obstacle feature information set in the first obstacle feature information set to obtain the first obstacle feature information set after deduplication. Wherein, the above-mentioned first obstacle feature information set after deduplication may be the first obstacle feature information set after redundancy removal. First, a clustering process is performed on each first obstacle feature information in the first obstacle feature information set to obtain a first clustered obstacle feature information set. Wherein, each first clustered obstacle characteristic information set in the first clustered obstacle characteristic information set may be used to represent the same obstacle vehicle. The first clustered obstacle feature information in the first clustered obstacle feature information group may be information corresponding to the same obstacle vehicle. According to the first horizontal distance value and the first longitudinal distance value included in each first obstacle characteristic information, the above-mentioned first obstacle characteristic information set can be clustered through a preset clustering algorithm to obtain the first cluster Obstacle feature information set. Then, for each first clustered obstacle feature information group in the first clustered obstacle feature information set, any first clustered obstacle feature information in the first clustered obstacle feature information set is determined as The feature information of the first obstacle is de-redundant.

As an example, the aforementioned preset clustering algorithm may include but not limited to at least one of the following: a density-based clustering algorithm and a K-means algorithm.

The second step is to perform deduplication processing on each second obstacle characteristic information in the above-mentioned second obstacle characteristic information set to obtain a deduplicated second obstacle characteristic information set. Firstly, a clustering process is performed on each second obstacle feature information in the second obstacle feature information set to obtain a second clustered obstacle feature information set. Wherein, each second clustered obstacle feature information set in the second clustered obstacle feature information set may be used to represent the same obstacle vehicle. The second clustered obstacle feature information in the second clustered obstacle feature information group may be information corresponding to the same obstacle vehicle. According to the second horizontal distance value and the second longitudinal distance value included in each second obstacle feature information, the above-mentioned second obstacle feature information set can be clustered through the above-mentioned preset clustering algorithm to obtain the second cluster Cluster obstacle feature information group set. Then, for each second clustered obstacle feature information set in the second clustered obstacle feature information set, any second clustered obstacle feature information in the second clustered obstacle feature information set is determined as The second obstacle feature information is deduplicated.

Step 3: For each de-redundancy first obstacle feature information set in the above-mentioned de-redundancy first obstacle feature information set, combine the above-mentioned de-redundancy second obstacle feature information set with the above-mentioned de-redundancy first obstacle feature information set The deduped second obstacle feature information matching the feature information is determined as the obstacle feature information to be deleted. Wherein, matching with the first obstacle characteristic information after deduplication may be the second lateral distance value included in the second obstacle characteristic information after deduplication and the second lateral distance value included in the first obstacle characteristic information after deduplication The error value between satisfies the preset error threshold, and the error value between the second longitudinal distance value included in the second obstacle feature information after deduplication and the second longitudinal distance value included in the first obstacle feature information after deduplication satisfies Preset error threshold. The aforementioned preset error threshold may be a preset error threshold. For example, the aforementioned preset error threshold may be 0.1 meters. The above-mentioned obstacle feature information to be deleted may be deduped second obstacle feature information waiting to be deleted.

The fourth step is to delete the above-mentioned second obstacle characteristic information after deduplication that matches the obtained characteristic information of each obstacle to be deleted in the second obstacle characteristic information set after deduplication, and obtain the second obstacle characteristic information after deletion set. Wherein, matching with the obtained characteristic information of each obstacle to be deleted may be that the second obstacle characteristic information after deduplication is the same as any characteristic information of the obstacle to be deleted. The deleted second obstacle characteristic information in the above-mentioned deleted second obstacle characteristic information set may be deduped second obstacle characteristic information that is different from each deduplicated first obstacle characteristic information. Firstly, for each obtained obstacle feature information to be deleted, the deduped second obstacle feature information that is the same as the obstacle feature information to be deleted may be deleted from the deduped second obstacle feature information set. Then, the deleted second obstacle feature information set after deduplication is determined as the deleted second obstacle feature information set.

The fifth step is to generate a target obstacle feature information set based on the above-mentioned first obstacle feature information set after deduplication and the above-mentioned deleted obstacle feature information set. Wherein, the target obstacle feature information in the above target obstacle feature information set can be used to characterize different obstacle vehicles. Each de-redundant first obstacle characteristic information in the above-mentioned first obstacle characteristic information set after deduplication, and each deleted second obstacle characteristic information in the above-mentioned deleted second obstacle characteristic information set can be determined as the target The feature information of the obstacle is obtained to obtain the feature information set of the target obstacle.

The sixth step is to classify the target obstacle feature information set based on the lane line perception information, and obtain the current lane obstacle feature information group and the left lane obstacle feature information group. The current lane obstacle feature information set and the left lane obstacle feature information set can be obtained through a preset classification method. Among them, the above-mentioned preset classification method can first project the lane line and each obstacle vehicle to the vehicle body coordinate system according to the characteristic information of the lane line and each obstacle vehicle, and then distinguish the obstacles corresponding to different lanes according to the coordinate position relationship method of the vehicle. Specifically, the above preset classification method can be implemented as follows:

The first sub-step, based on the preset camera transformation matrix, project the left lane line curve and the right lane line curve included in the above lane line perception information to the vehicle body coordinate system to obtain the left projected lane line curve and right projection Lane line curve. Wherein, the aforementioned preset camera transformation matrix may be a predefined camera transformation matrix. The above preset camera transformation matrix can be used to characterize the coordinate transformation relationship between the camera coordinate system and the vehicle body coordinate system. The above-mentioned left projected lane line curve can be used to represent the left lane line of the lane where the current vehicle is located. The above-mentioned right projected lane line curve can be used to represent the right lane line of the lane where the current vehicle is located.

The second sub-step is to generate an obstacle coordinate set based on the above target obstacle feature information set. Wherein, the obstacle coordinates in the above obstacle coordinate set may be the coordinates of the obstacle vehicle in the vehicle body coordinate system. The obstacle coordinates in the above obstacle coordinate set may include a horizontal axis coordinate value and a vertical axis coordinate value. For each target obstacle feature information in the target obstacle feature information set, it can be determined according to the azimuth, lateral distance and longitudinal distance between the obstacle vehicle and the current vehicle in the target obstacle feature information The corresponding obstacle coordinates in the car body coordinate system.

The third sub-step, for each obstacle coordinate in the above obstacle coordinate set, the following steps can be performed:

Step 1: Determine the vertical axis coordinate value included in the obstacle coordinates as the target vertical axis coordinate value.

Step 2: Determine the abscissa coordinate value of a point on the above left projected lane line curve that satisfies the first preset coordinate value condition as the first left abscissa coordinate value. Wherein, the above-mentioned first preset coordinate value condition may be that the vertical axis coordinate value of the point on the left projected lane line curve is the target vertical axis coordinate value.

Step 3: Determine the abscissa coordinate value of a point on the above-mentioned right projected lane line curve that satisfies the second preset coordinate value condition as the first right abscissa coordinate value. Wherein, the above-mentioned second preset coordinate value condition may be that the vertical axis coordinate value of the point on the right projected lane line curve is the target vertical axis coordinate value.

Step 4: In response to determining that the coordinate values of the horizontal axis included in the obstacle coordinates satisfy the preset coordinate value interval condition, determine the target obstacle feature information corresponding to the obstacle coordinates as the obstacle feature information of the current lane. Wherein, the above preset coordinate value interval condition may be that the abscissa coordinate value included in the obstacle coordinates is between the first left abscissa coordinate value and the first right abscissa coordinate value.

Step 5, in response to determining that the abscissa coordinate value included in the obstacle coordinates is smaller than the first left abscissa coordinate value, determine the target obstacle feature information corresponding to the obstacle coordinates as the left lane obstacle feature information.

Step 103, based on the current vehicle perception information and the current lane obstacle feature information group, generate lane change expectation information.

In some embodiments, the execution subject may generate lane change expectation information based on the current vehicle perception information and the current lane obstacle feature information group in various ways. Wherein, the above-mentioned lane change expectation information may be information on whether the current speed of the vehicle needs to reach a preset speed value through lane change.

In some optional implementations of some embodiments, the foregoing current vehicle perception information may include a vehicle speed value. Wherein, each piece of current lane obstacle feature information in the above current lane obstacle feature information group may include a longitudinal distance value. The above-mentioned longitudinal distance value may be a first longitudinal distance value or a second longitudinal distance value. The above-mentioned executive body can generate lane-changing expectation information through the following steps:

The first step is to select the current lane obstacle characteristic information satisfying the preset forward orientation condition from the above-mentioned current lane obstacle characteristic information group as the front obstacle vehicle characteristic information, and obtain the front obstacle vehicle characteristic information group. Wherein, the aforementioned preset forward orientation condition may be that the orientation identifier included in the current lane obstacle characteristic information is the same as any preset orientation identifier in the preset orientation identifier group. The preset orientation identifiers in the preset orientation identifier group may be preset orientation identifiers. The vehicle characteristic information of the obstacle ahead in the above-mentioned vehicle characteristic information group of the obstacle ahead is used to represent the obstacle vehicle driving in front of the current vehicle on the current lane.

The second step is to select the vehicle characteristic information of the obstacle ahead satisfying the preset longitudinal distance condition from the vehicle characteristic information group of the obstacle ahead as the vehicle characteristic information of the target obstacle. Wherein, the above-mentioned preset longitudinal distance condition may be that the longitudinal distance value included in the vehicle characteristic information of the obstacle ahead is the minimum value among the longitudinal distance values included in the vehicle characteristic information group of the obstacle ahead. The above feature information of the target obstacle vehicle may be used to characterize the obstacle vehicle in front of the current vehicle on the current lane with the smallest distance value from the current vehicle.

The third step is to obtain the speed threshold corresponding to the longitudinal distance value included in the vehicle characteristic information of the target obstacle. Wherein, the aforementioned speed threshold may be a maximum boundary value of the vehicle speed. The relationship table between the speed threshold and the longitudinal distance value in the cache may be read, and the speed threshold corresponding to the longitudinal distance value included in the vehicle characteristic information of the target obstacle may be obtained therefrom. Wherein, the above speed threshold and longitudinal distance value relationship table may include speed distance relationship records. The above speed-distance relationship record can be used to characterize the one-to-one correspondence between the speed threshold and the longitudinal distance value.

In a fourth step, in response to determining that the vehicle speed value included in the current vehicle perception information is less than or equal to the above speed threshold, determine whether the preset expected speed value is greater than the above speed threshold. Wherein, the above-mentioned preset expected speed value may be a preset vehicle speed value expected by the driver. First, the difference between the preset expected speed value and the speed threshold is determined as the speed difference. Wherein, the above-mentioned speed difference may be the difference between the maximum speed value that can be achieved and the vehicle speed value expected by the driver when there is no risk of collision between the current vehicle and the obstacle vehicle. Then, if the speed difference is greater than 0, the preset expected speed value is greater than the speed threshold. Finally, if the aforementioned speed difference is less than or equal to 0, then the aforementioned preset expected speed value is less than or equal to the aforementioned speed threshold.

Step 5: In response to determining that the preset expected speed value is greater than the speed threshold, determine the preset expected speed value and the preset left lane-changing sign as lane-changing expected information. Wherein, the above preset left lane change sign may be used to represent a left lane change. For example, the above-mentioned preset left lane change sign can be represented by 1.

Optionally, in response to determining that the preset expected speed value is less than or equal to the above speed threshold, the execution subject may determine the above speed threshold and the preset no lane change flag as the lane change expectation information. Wherein, the above-mentioned preset no-lane-changing sign can be used to indicate that the current vehicle does not change lanes and continues to drive in the current lane. For example, the above preset no-lane-changing flag may be represented by 0.

Step 104, in response to determining that the expected lane change information satisfies a preset expected lane change condition, based on the current vehicle perception information and the lane path information sequence, lane merging information is generated.

In some embodiments, the execution subject may generate lane merging information based on the current vehicle perception information and the lane path information sequence in response to determining that the lane change expectation information satisfies a preset expected lane change condition. Wherein, the above-mentioned preset expected lane-changing condition may be that the above-mentioned lane-changing expected information includes a preset left lane-changing sign. The above-mentioned merging information may be information required for vehicles merging. Merge information can be generated through the following steps:

The first step is to obtain the map lane identification corresponding to the current vehicle coordinates included in the above-mentioned current vehicle perception information. Wherein, the above-mentioned map lane identification may be the identification of the lane where the current vehicle is located on the high-precision map. The map lane marking corresponding to the current vehicle coordinates can be obtained through the HD map interface.

The second step is to determine the serial number corresponding to the lane path information matching the above-mentioned map lane mark in the above-mentioned lane path information sequence as the current lane number, and determine the lane path information matching the above-mentioned map lane mark in the above-mentioned lane path information sequence determined as the current lane path information. Wherein, the matching with the above-mentioned lane markers on the map may mean that the lane markers included in the lane path information are the same as the lane markers on the map. The above current lane path information may be used to characterize the lane where the current vehicle is located. The above-mentioned serial number of the current lane may be a serial number of the current lane in the lane path.

In the third step, the sum of the above-mentioned current lane number and 1 is determined as the target lane number.

In the fourth step, based on the serial number of the target lane, the following coordinate generation steps of the starting end of the merging lane are performed:

The first sub-step is to determine the lane path information whose serial number is the serial number of the target lane in the above lane path information sequence as the target lane path information.

In the second sub-step, in response to determining that the adjacent lane information group included in the target lane path information satisfies a preset adjacent lane condition, determine the starting coordinates of the lane corresponding to the target lane path information as the starting end coordinates of the merging lane. Wherein, the above-mentioned preset adjacent lane condition may be that there is no sign of the left adjacent lane in the adjacent lane information group included in the target lane path information.

The fifth step is to obtain the target path length value based on the above-mentioned current vehicle coordinates and the above-mentioned starting end coordinates of the merging lane. Wherein, the aforementioned target path length value may be the length value of the path of the current vehicle from the coordinates of the current vehicle to the coordinates of the starting end of the merging lane. The target path length value from the above-mentioned current vehicle coordinates to the above-mentioned starting end coordinates of the merging lane can be obtained through vehicle navigation.

Step 6: Determine the coordinates of the starting end of the merging lane, the sequence number of the target lane, the path information of the target lane, and the length of the target path as merging information.

Optionally, the above execution subject may also perform the following steps:

In the first step, in response to determining that the adjacent lane information group included in the target lane path information does not satisfy the preset adjacent lane condition, determine the difference between the target lane number and 1 as the preceding lane number. Wherein, the sequence number of the preceding driving lane may correspond to the lane path information ranked before the target lane path information in the lane path information sequence.

The second step is to determine whether the road sign corresponding to the above-mentioned target lane path information is the same as the road sign corresponding to the lane path information whose sequence number is the number of the preceding lane in the above-mentioned lane path information sequence.

In the third step, in response to determining that the road sign corresponding to the above-mentioned target lane path information is different from the road sign corresponding to the lane path information whose sequence number is the serial number of the preceding lane in the above-mentioned lane path information sequence, start the lane corresponding to the above-mentioned target lane path information The coordinates are determined as the coordinates of the starting end of the merging lane.

Optionally, in response to determining that the road sign corresponding to the target lane path information is the same as the road sign corresponding to the lane path information whose serial number is the serial number of the preceding lane in the above lane path information sequence, the above-mentioned target lane path information is stored in The sum of the sequence number and 1 in the above lane route information sequence is determined as the target lane number, and the above step of generating coordinates of the starting end of the merging lane is executed again.

Step 105, in response to determining that the lane merging information satisfies the preset distance condition, perform detection processing on the obstacle characteristic information group on the left lane, and obtain lane changing decision information.

In some embodiments, in response to determining that the merging information satisfies the preset distance condition, the execution subject may detect and process the obstacle feature information group in the left lane in various ways to obtain lane change decision information. Wherein, the aforementioned preset distance condition may be that the target path length value included in the merging information is greater than or equal to the preset distance value. The aforementioned preset distance value may be a preset distance value. For example, the aforementioned preset distance value may be 1.5 kilometers.

Optionally, each left lane obstacle characteristic information in the left lane obstacle characteristic information group may further include a longitudinal relative speed value and a lateral relative speed value. Wherein, the above-mentioned longitudinal relative speed value may be a relative speed value from the current vehicle to the obstacle vehicle in the longitudinal direction. The above-mentioned lateral relative speed value may be a relative speed value from the current vehicle to the obstacle vehicle in the lateral direction. The above lane change decision information can be used to characterize whether there is a collision risk in the left lane. The following steps can be used to detect and process the above-mentioned left lane obstacle feature information group to generate lane-changing decision information:

The first step is to generate a comprehensive distance value group for obstacles on the left based on the above-mentioned characteristic information group of obstacles in the left lane. Wherein, the left obstacle comprehensive distance value in the above left obstacle comprehensive distance value group may be the distance value between the center of the front axle of the current vehicle and the center of the front axle of the obstacle vehicle. For each left lane obstacle feature information in the left lane obstacle feature information group, the first lateral distance value and the first longitudinal distance value corresponding to the left lane obstacle feature information can be used according to the triangular distance formula , or the second lateral distance value and the second longitudinal distance value corresponding to the above-mentioned left lane obstacle feature information to generate a left comprehensive obstacle distance value.

The second step is to select the comprehensive distance value of the left obstacle that satisfies the preset minimum distance value condition from the above-mentioned comprehensive distance value group of the left obstacle, and select the left side corresponding to the selected comprehensive distance value of the left obstacle. The lane obstacle feature information is determined as the left target obstacle feature information. Wherein, the preset minimum distance value condition may be that the left obstacle comprehensive distance value is the minimum value in the left obstacle comprehensive distance value group. The above left target obstacle feature information can be used to characterize the obstacle vehicle on the left lane with the smallest distance value from the current vehicle.

The third step is to generate the left collision duration value based on the characteristic information of the left target obstacle. Wherein, the above-mentioned left collision duration value may be a duration value required for a collision between the current vehicle and the obstacle vehicle. Firstly, the quotient of the first longitudinal distance value or the second longitudinal distance value and the longitudinal relative speed value included in the characteristic information of the left target obstacle is determined as the longitudinal risk duration value. Wherein, the above-mentioned longitudinal risk duration value may be the duration value required for longitudinal collision between the current vehicle and the obstacle vehicle. Then, the quotient of the first lateral distance value or the second lateral distance value included in the characteristic information of the left target obstacle and the lateral relative speed value is determined as the lateral risk duration value. Wherein, the above-mentioned lateral risk duration value may be a duration value required for a lateral collision between the current vehicle and the obstacle vehicle. Finally, the minimum value of the above-mentioned longitudinal risk duration value and the above-mentioned lateral risk duration value is determined as the left collision duration value.

The fourth step is to generate lane change decision information based on the above left collision duration value. Firstly, in response to determining that the left collision duration value is greater than the preset duration threshold, the preset lane changeable sign is determined as the lane change decision information. Wherein, the preset duration threshold may be a preset threshold. For example, the aforementioned preset duration threshold may be 2 seconds. The above-mentioned preset lane-changeable sign can be used to indicate that the current vehicle can change lanes to the left lane. Then, in response to determining that the left collision duration value is less than or equal to the preset duration threshold value, the preset lane-change-unable flag is determined as lane-changing decision information. Wherein, the above-mentioned preset lane-changing-unable sign can be used to indicate that the current vehicle cannot change lanes to the left lane.

Step 106, in response to determining that the lane-changing decision information satisfies the preset lane-changing conditions, generate lane-changing trajectory information based on the preset high-precision map, current vehicle perception information, left lane obstacle feature information group, and lane line perception information.

In some embodiments, the execution subject may respond to determining that the lane change decision information meets the preset lane change conditions, based on the preset high-precision map, the current vehicle perception information, the left lane obstacle characteristic information group and the above Lane line perception information to generate lane change trajectory information. Wherein, the above-mentioned preset lane-changing condition may be that the lane-changing decision information includes a preset lane-changing flag. The above lane change trajectory information can be used to characterize the path of the current vehicle changing lanes from the current position to the centerline of the left lane. The lane changing trajectory information may include but not limited to at least one of the following: lane changing trajectory curve, starting point coordinates, end point coordinates and lane separation point coordinates. The above-mentioned lane change trajectory curve can be used to characterize the path of the vehicle changing lanes from the current lane to the adjacent lane. The aforementioned starting point coordinates may be the current vehicle coordinates. The above-mentioned end point coordinates may be the coordinates at the end of the current vehicle's lane change in the high-definition map coordinate system. The above-mentioned coordinates of the lane separation point may be coordinates of a point on the lane line in the high-precision map coordinate system that divides the lane-changing trajectory curve into two segments. The lane-changing trajectory information can be generated based on the preset high-precision map, the above-mentioned current vehicle perception information, the above-mentioned left lane obstacle characteristic information group, and the above-mentioned lane line perception information through a preset lane-changing trajectory planning algorithm.

As an example, the aforementioned preset lane-changing trajectory planning algorithm may include but not limited to at least one of the following: a sine function lane-changing trajectory, a polynomial function lane-changing trajectory.

Step 107, based on the lane change trajectory information, lane merging information and lane path information sequence, generate vehicle path information, and send the vehicle path information to the vehicle control module for controlling vehicle driving, wherein generating the vehicle path information includes:

Step 1071: Generate merging trajectory information based on the lane change trajectory information and the merging information.

In some embodiments, the execution subject may generate the merge trajectory information based on the lane change trajectory information and the merge information. Wherein, the above-mentioned merging trajectory information may be the path trajectory information from the end point coordinates included in the lane change trajectory information to the starting end coordinates of the merging lane included in the merging information. The merge trajectory information can be generated through the following steps:

The first step is to generate driving area information based on the target lane map information, the current lane path information, the lane change track information, and the merge information. Wherein, the above-mentioned driving area information may be the information of the expected passing area of the vehicle merging. First, determine the straight line perpendicular to the driving direction of the vehicle and passing through the coordinates of the end point included in the lane-changing track information as the first straight line, and determine the line perpendicular to the driving direction of the vehicle and passing through the coordinates of the starting end of the merging lane included in the lane-changing information The straight line is determined as the second straight line. Secondly, the coordinates of the intersection point between the first straight line and the left lane line curve of the lane where the coordinates of the above-mentioned end point are located are determined as the coordinates of the first intersection point, and the coordinates of the intersection point between the first straight line and the right lane line curve of the lane where the coordinates of the above-mentioned end point are located The coordinates of the intersecting point are determined as the coordinates of the second intersection point. Then, the coordinates of the intersection point between the second straight line and the left lane line curve of the lane where the coordinates of the starting end of the merging lane are located are determined as the coordinates of the third intersection point, and the coordinates of the first straight line and the coordinates of the starting end of the merging lane are determined as the coordinates of the intersection point. The coordinates of the intersection between the right lane line curves are determined as the fourth intersection coordinates. Afterwards, a straight line passing through the coordinates of the first intersection point and the coordinates of the third intersection point is determined as a third straight line, and a straight line passing through the coordinates of the second intersection point and the fourth intersection point is determined as a fourth straight line. Finally, the information of the closed area surrounded by the first straight line, the second straight line, the third straight line and the fourth straight line is determined as the driving area information.

The second step is to generate a boundary line segment group and a triangular area information set based on the above driving area information. Wherein, the boundary line segment in the above boundary line segment group may be a line segment corresponding to a common boundary between two regions. The triangular area information in the above triangular area information set may be the information of the triangular area that the vehicle is expected to pass through when merging. First, the coordinates of the midpoint of the second intersection coordinates and the third intersection coordinates are determined as target midpoint coordinates. Then, the coordinates of the first intersection point, the second intersection point, the third intersection point and the fourth intersection point are respectively connected with the target midpoint coordinates to obtain the boundary information group. Wherein, the boundary information in the above boundary information group may be used to represent a line segment on the common boundary of two regions. Finally, according to each boundary information in the above-mentioned boundary information group, the closed area corresponding to the above-mentioned driving area information is divided into various triangular areas, and the information corresponding to each triangular area is determined as triangular area information to obtain a triangular area information set.

In the third step, a connected region information group may be generated based on the above boundary information group and the above triangular region information set through a preset connected region identification algorithm. Wherein, the connected region information in the connected region information group includes the connected boundary segment information group and the target triangular region information sequence. The connected boundary line segment information in the above connected boundary line segment information group may be the information of the line segment on the common boundary between two regions. The connected boundary line segment information in the connected boundary line segment information group may include first end point coordinates and second end point coordinates. The aforementioned first end point coordinates may be the coordinates of an end point of a line segment. The aforementioned second end point coordinates may be the coordinates of the other end point of the line segment. The above target triangular area information sequence can be used to characterize the connected area covering the end coordinates included in the lane change trajectory information and the starting end coordinates of the merging lane included in the merging information. The aforementioned preset connected region identification algorithm may be a preset algorithm for identifying the connectivity of each triangular area corresponding to the aforementioned triangular area information set according to the aforementioned boundary information set.

In the fourth step, based on the above-mentioned connected area information group, generate the merge trajectory information group to be screened. Wherein, the merging trajectory information to be screened in the above-mentioned merging trajectory information group to be screened may be any path trajectory information from the end point coordinates included in the lane changing trajectory information to the starting end coordinates of the merging lane included in the merging information. The merged track information to be screened in the merged track information group to be screened may include a track path length value. The above track path length value may be the length value of the path corresponding to the curved track. For each connected region information in the above connected region information group, the following steps can be performed:

The first sub-step is to generate a merge key point coordinate group based on the connected boundary segment information group included in the connected area information. Wherein, the merging key point coordinates in the above merging key point coordinate group may be the midpoint coordinates of the boundary line segment. For each connected boundary line segment information in the connected boundary line segment information group included in the connected area information, the coordinates of the midpoint between the first end point coordinates and the second end point coordinates included in the above connected boundary line segment information can be determined as the merged path key point coordinates.

The second sub-step is to generate the merging track information to be screened based on the coordinate group of key points of the merging, the end point coordinates included in the lane change track information, and the starting end coordinates of the merging lane included in the merging information. The trajectory information to be screened and merged can be generated through the preset lane-changing trajectory planning algorithm described above.

The fifth step is to set the path length value of each track in the above-mentioned combined track information group to be screened as the track path length value group to be screened, and select the track to be screened that meets the preset filter conditions from the above-mentioned track path length value group to be screened The path length value is used as the target length value. Wherein, the path length value of the path to be screened in the path length value group of the path to be screened may be the length value of the path path from the end point coordinates included in the lane change track information to the start end coordinates of the merge lane included in the merge information. The aforementioned preset filtering condition may be that the path length value of the track to be filtered is the minimum value in the path length value group of the track to be filtered. The above-mentioned target length value may be the minimum value in the path length value group of the track to be filtered.

In the sixth step, the merging track information corresponding to the target length value to be screened is determined as the merging track information.

Step 1072, based on the merging information, intercept the lane path information sequence to obtain the intercepted lane path information sequence.

In some embodiments, the execution subject may intercept the lane path information sequence based on the merge information to obtain the intercepted lane path information sequence. Wherein, the intercepted lane path information in the above intercepted lane path information sequence may be information about the lane and the path on the lane that the vehicle will drive after completing lane changing and merging. According to the target lane number included in the above-mentioned merge information, starting from the lane path information whose sequence number is the target lane number in the above-mentioned lane path information sequence, intercepting to the end of the above-mentioned lane path information sequence, the intercepted lanes arranged in sequence The path information is determined as the path information sequence of the intercepted rear lane.

Step 1073, determine the lane change trajectory information, the merge trajectory information and the intercepted lane path information sequence as the vehicle path information.

In some embodiments, the execution subject may determine the lane change trajectory information, the merge trajectory information, and the intercepted lane path information sequence as the vehicle path information. Wherein, the above vehicle route information may be used to characterize the route of the current vehicle from the current position to the destination.

In practice, the execution subject may send the vehicle route information to the vehicle control module, and then control the vehicle to drive along the route represented by the vehicle route information through vehicle control instructions. Wherein, the above-mentioned vehicle control module may be a set of programs that control the running of the vehicle through vehicle control instructions. The above vehicle control instructions may include but not limited to at least one of the following: acceleration instructions, deceleration instructions and steering instructions.

As an inventive point of the embodiment of the present disclosure, the above-mentioned vehicle route information generating step and its related content solve the technical problem 2 "computing resources are occupied more" mentioned in the background art. The factors that lead to a large occupation of computing resources are often as follows: If the vehicle changes lanes halfway to increase the speed, it usually needs to rely on the car navigation to regenerate the vehicle route information based on the vehicle location and destination, resulting in a large occupation of computing resources. If the above factors are resolved, the effect of reducing the occupation of computing resources can be achieved. In order to achieve this effect, the disclosure first plans the lane-changing trajectory of the vehicle. Then, the merging trajectory planning is carried out between the coordinates of the end point of the lane changing trajectory and the starting end of the merging lane. Among them, when planning the merging trajectory, first determine the driving area between each lane, and then divide the driving area into several triangular areas through the boundary line segment, and then determine the coordinates of the end point covering the lane changing trajectory and the starting end of the merging lane The connected area composed of multiple triangular areas, and the path trajectory to be screened are determined on the basis of the connected area. Since there are multiple determined connected regions, multiple path trajectories can be obtained, and the path trajectory with the smallest length value among the path trajectories is determined as the merge trajectory. Thus, the shortest merge path can be obtained. Afterwards, according to the merging information, the above-mentioned lane path information sequence may be intercepted to obtain the shortest path from the completion of the merging to the destination. Finally, the above lane change trajectory information, the above lane merge trajectory information and the above intercepted lane path information sequence are determined as vehicle path information. Therefore, segmental planning can be carried out corresponding to the vehicle route, which can not only drive according to the shortest route as much as possible, but also avoid repeated route planning for some road sections, thereby reducing the occupation of computing resources. Furthermore, the operating efficiency of the system can be improved.

Optionally, the above-mentioned lane change trajectory information may include end point coordinates. The above executive body can also perform the following steps:

The first step is to project the lane separation point coordinates included in the lane change track information to the preset vehicle body coordinate system to obtain the lane separation point projected coordinates. Wherein, the aforementioned preset vehicle body coordinate system may be a preset vehicle body coordinate system at the current moment. The coordinates of the lane separation point included in the lane change track information can be projected from the high-precision map coordinate system to the vehicle body coordinate system through a preset transformation matrix to obtain the projected coordinates of the lane separation point. Wherein, the above-mentioned preset conversion matrix can be used to characterize the coordinate conversion relationship between the high-precision map coordinate system and the vehicle body coordinate system.

The second step is to obtain the first lane line feature information, the third obstacle feature information set, and the vehicle positioning coordinates corresponding to the above-mentioned current vehicle. Wherein, the above-mentioned first lane line feature information may be the information of the lane lines on both sides of the lane where the current vehicle is located outputted by the smart camera. The above-mentioned third obstacle feature information set may be the information of obstacle vehicles around the current vehicle output by the smart camera. The third obstacle characteristic information in the third obstacle characteristic information set may include obstacle lateral distance values and obstacle longitudinal distance values. The above-mentioned vehicle positioning coordinates may be the GPS positioning coordinates of the current vehicle output by the vehicle navigation device.

The third step is to classify the third obstacle feature information set based on the first lane line feature information to obtain the first lane obstacle feature information set and the second lane obstacle feature information set. Wherein, the first lane obstacle feature information in the first lane obstacle feature information set may be used to characterize the obstacle vehicle on the current lane where the current vehicle is located. The first lane obstacle characteristic information in the first lane obstacle characteristic information set may include a first target lateral distance value, a first target longitudinal distance value, a first target lateral relative speed value, and a first target longitudinal relative speed value. The above-mentioned second lane obstacle feature information set in the second lane obstacle feature information set may be used to characterize the obstacle vehicle on the left adjacent lane of the lane where the current vehicle is located. The second lane obstacle characteristic information in the second lane obstacle characteristic information set may include a second target lateral distance value, a second target longitudinal distance value, a second target lateral relative speed value, and a second target longitudinal relative speed value. The aforementioned initial obstacle feature information set may be classified by the aforementioned preset classification method to obtain the first lane obstacle feature information set and the second lane obstacle feature information set.

In a fourth step, in response to determining that the projected coordinates of the lane separation point meet the first preset projected coordinate condition, a target collision duration value is generated based on the second lane obstacle feature information set. Wherein, the first preset projected coordinate condition may be that the coordinate value of the horizontal axis corresponding to the projected coordinates of the lane separation point is less than or equal to 0 and the coordinate value of the vertical axis is greater than or equal to 0. The above target collision duration value can be used to represent how long it will take for the current vehicle to collide with the obstacle vehicle. First, for each second lane obstacle feature information in the second lane obstacle feature information set, a target obstacle comprehensive distance value may be generated based on the second lane obstacle feature information according to a triangle distance formula. Among the generated target obstacle comprehensive distance values, the target obstacle comprehensive distance value may be the distance value between the center of the front axle of the current vehicle and the center of the front axle of the obstacle vehicle. Secondly, the second lane obstacle characteristic information corresponding to the target obstacle comprehensive distance value with the smallest median among all target obstacle comprehensive distance values is determined as the second target obstacle characteristic information. Then, determine the quotient of the second target longitudinal distance value included in the above-mentioned second target obstacle characteristic information and the second target longitudinal relative speed value as the target longitudinal risk duration value, and determine the second target obstacle characteristic information included in the above-mentioned second target obstacle. The quotient of the two target lateral distance values and the second target lateral relative speed value is determined as the target lateral risk duration value. Finally, the minimum value of the target longitudinal risk duration value and the target lateral risk duration value is determined as the target collision duration value.

Step 5: In response to determining that the target collision duration value is less than the preset duration threshold value, a first obstacle avoidance trajectory plan is generated based on the vehicle positioning coordinates, the first lane line feature information and the first lane obstacle feature information set information, and sending the above-mentioned first obstacle avoidance trajectory planning information to the above-mentioned vehicle control module for controlling the driving of the vehicle. Wherein, the above-mentioned first obstacle avoidance trajectory planning information may be used to represent the path traveled by the current vehicle from the current position back to the centerline of the current lane after the lane change is suspended. The first obstacle avoidance trajectory planning information can be generated through the preset path planning algorithm, and the first obstacle avoidance trajectory planning information can be sent to the vehicle control module, and the vehicle can be controlled to drive back to the center line of the current lane through the vehicle control command keep driving.

As an example, the aforementioned preset path planning algorithm may include but not limited to at least one of the following: an A* (A-Star) path planning algorithm, and a Lattice Planner planning algorithm.

Optionally, the above execution subject may also perform the following steps:

The first step is to obtain a target vehicle speed value in response to determining that the projection coordinates of the above-mentioned lane separation points satisfy the second preset projection coordinate condition. Wherein, the second preset projected coordinate condition may be that the coordinate value of the horizontal axis corresponding to the projected coordinates of the lane separation point is greater than 0 and the coordinate value of the vertical axis is less than 0. The aforementioned target vehicle speed value may be a current vehicle speed value. The target vehicle speed value can be obtained through the inertial navigation equipment.

In the second step, a remaining distance value is generated based on the above-mentioned vehicle positioning coordinates and the end point coordinates included in the above-mentioned lane change track information. Wherein, the above-mentioned remaining distance value may be the length value of the path that the current vehicle still needs to travel after completing the lane change. Firstly, the length value of the path between the above-mentioned vehicle positioning coordinates and the end point coordinates included in the above-mentioned lane-changing track information can be obtained through the interface of the high-precision map. Then, determine the obtained length value as the remaining distance value.

In the third step, the quotient of the above-mentioned remaining distance value and the above-mentioned target vehicle speed value is determined as the remaining lane-changing duration value. Wherein, the above-mentioned remaining lane-changing duration value may be the duration value still required for the current vehicle to complete the lane-changing.

The fourth step is to generate a remaining collision duration value based on the above first lane obstacle feature information set. Wherein, the above-mentioned remaining collision duration value may be used to represent how long it will take for the current vehicle to collide with the obstacle vehicle. First, for each first lane obstacle feature information in the first lane obstacle feature information set, a first comprehensive obstacle distance value may be generated based on the first lane obstacle feature information according to a triangle distance formula. Wherein, the first integrated obstacle distance value among the generated first integrated obstacle distance values may be the distance value between the center of the front axle of the current vehicle and the center of the front axle of the obstacle vehicle. Secondly, the first lane obstacle characteristic information corresponding to the first obstacle comprehensive distance value with the smallest median among the first obstacle comprehensive distance values is determined as the first target obstacle characteristic information. Then, determine the quotient of the first target longitudinal distance value included in the first target obstacle characteristic information and the first target longitudinal relative speed value as the first target longitudinal risk duration value, and include the above first target obstacle characteristic information The quotient of the first target lateral distance value and the first target lateral relative speed value is determined as the first target lateral risk duration value. Finally, the smaller value of the first target longitudinal risk duration value and the first target lateral risk duration value is determined as the remaining collision duration value.

In the fifth step, the difference between the above-mentioned remaining collision duration value and the above-mentioned remaining lane-changing duration value is determined as a duration difference value. Wherein, the above-mentioned duration difference can be used to represent whether the current vehicle has a collision risk during the process of continuing to change lanes.

Step 6: In response to determining that the above-mentioned duration difference is less than or equal to the preset duration difference threshold, a second obstacle avoidance trajectory is generated based on the above-mentioned vehicle positioning coordinates, the above-mentioned first lane line feature information, and the above-mentioned first lane obstacle feature information set Planning information, and sending the above-mentioned second obstacle avoidance trajectory planning information to the above-mentioned vehicle control module for controlling the driving of the vehicle. Wherein, the aforementioned preset duration difference threshold may be a preset threshold. For example, the aforementioned preset duration difference threshold may be 2 seconds. The above-mentioned second obstacle avoidance trajectory planning information can be used to represent the path replanned by the vehicle to avoid obstacles when the current vehicle has changed lanes to the left lane but has not yet completed the lane change process. The second obstacle avoidance trajectory planning information can be generated through the above-mentioned preset path planning algorithm, and the above-mentioned second obstacle avoidance trajectory planning information can be sent to the above-mentioned vehicle control module, and the vehicle can be controlled to drive safely to the center line of the current lane through the vehicle control command superior.

The steps of generating the first obstacle avoidance trajectory planning information and the second obstacle avoidance trajectory planning information and their related contents as an invention point of the embodiment of the present disclosure solve the technical problem three mentioned in the background technology "reducing the safety of vehicle driving" ". Factors that reduce the safety of vehicles are often as follows: During the process of changing lanes, there may be unexpected situations such as the sudden deceleration of the vehicle in front of the target lane or the sudden acceleration of the vehicle behind the target lane. Changing lanes may collide with surrounding vehicles, reducing the safety of the vehicle. If the above-mentioned factors are solved, the effect of improving the safety of vehicle driving can be achieved. In order to achieve this effect, the present disclosure first uses the lane separation point coordinates included in the lane change trajectory information as a reference to determine whether the current vehicle has crossed the left lane line during the lane change process. Then, if the current vehicle has not crossed the left lane line, generate a target collision duration value to determine whether there is a collision risk in the left lane. If there is a risk of collision in the left lane, the first obstacle avoidance trajectory planning information is generated for controlling the vehicle not to continue changing lanes and to continue driving safely in the current lane. Finally, if the current vehicle crosses the left lane line, generate the time difference between the remaining lane change duration and the remaining collision duration, so as to determine whether there is enough buffer time to avoid collision after completing the lane change. If it is determined that there is no buffer time after the lane change is completed, the second obstacle avoidance trajectory planning information is generated for the control vehicle to continue driving safely in the left lane. Therefore, during the lane change process, the target lane can be monitored in real time so that when there is a risk of collision, the current vehicle can plan the obstacle avoidance trajectory in time to reduce the risk of collision. Thus, the safety of vehicle running can be improved.

The above-mentioned embodiments of the present disclosure have the following beneficial effects: through the method for generating vehicle route information in some embodiments of the present disclosure, the time-consuming vehicle driving can be shortened. Specifically, the reason why the vehicle takes a long time to drive is that although the vehicle can drive to the destination along the planned route, the vehicle has to be slower than the expected speed in order to maintain a safe distance during the long-term follow-up process. Based on this, the method for generating vehicle route information in some embodiments of the present disclosure firstly acquires current vehicle perception information, lane line perception information, first obstacle feature information set, second obstacle feature information set, and lane path information sequence. Here, information about the current vehicle, the lane line on the road, and the obstacle vehicle can be obtained, so as to facilitate the subsequent generation of lane change decision information based on the above information to determine whether the current vehicle needs to change lanes to the vehicle on the left for acceleration. Secondly, based on the lane line perception information, the first obstacle feature information set and the second obstacle feature information set are fused to obtain the current lane obstacle feature information set and the left lane obstacle feature information set. In this way, it is convenient to subsequently generate lane change expectation information based on information of obstacle vehicles on the current lane, and generate lane change decision information based on information of obstacle vehicles on the left lane. Then, based on the above-mentioned current vehicle perception information and the above-mentioned current lane obstacle feature information group, lane change expectation information is generated. In response to determining that the lane change expectation information satisfies a preset expected lane change condition, lane merging information is generated based on the current vehicle perception information and the lane path information sequence. In response to determining that the merging information satisfies the preset distance condition, the above-mentioned left lane obstacle feature information group is detected and processed to obtain lane-changing decision information. Thus, it can be determined according to the lane-changing expectation information of the current vehicle whether the current vehicle needs to change lanes to the vehicle on the left for acceleration. Considering that the current vehicle needs to continue to drive closer to the destination, the merge information is further generated on the basis of the lane change expectation information, and then the lane change decision information is finally determined according to the information of the obstacle vehicle on the left lane , in order to determine whether to generate a lane-changing trajectory for the current vehicle according to the lane-changing decision information for the vehicle to change lanes to the left fast lane, so as to increase the speed of the vehicle and shorten the time spent on driving. Afterwards, in response to determining that the lane change decision information meets the preset lane change conditions, a lane change is generated based on the preset high-precision map, the current vehicle perception information, the left lane obstacle characteristic information group, and the lane line perception information. track information. Thus, it can be determined through the lane change decision information that the current vehicle will change lanes to the left lane, and based on this, generate lane change trajectory information for the current vehicle to perform a lane change operation. Finally, based on the above-mentioned lane change trajectory information, the above-mentioned lane-merging information and the above-mentioned lane path information sequence, vehicle path information is generated, and the above-mentioned vehicle path information is sent to the vehicle control module for controlling the driving of the vehicle. Wherein, the above-mentioned generation of vehicle path information includes: generating merging trajectory information based on the above-mentioned lane-changing trajectory information and the above-mentioned merging information; based on the above-mentioned merging information, intercepting the above-mentioned lane path information sequence to obtain the intercepted lane path information sequence; The above lane changing trajectory information, the above lane merging trajectory information and the above intercepted lane path information sequence are determined as vehicle path information. In this way, vehicle route information can be generated for the vehicle to maintain a relatively high speed, thereby shortening the driving time. Therefore, the method for generating vehicle route information of the present disclosure can enable the vehicle to increase the driving speed by changing lanes to the fast lane on the premise of maintaining a safe distance from the preceding vehicle. And because the vehicles after changing lanes still need to drive towards the direction close to the destination, the consideration of the merging information is added when determining the lane changing decision information, and the merging information is also considered when generating the vehicle route information. Make sure that the vehicle follows the shortest route planned when the vehicle is at the departure point as far as possible. Therefore, the time-consuming driving of the vehicle can be shortened. Furthermore, the efficiency of vehicle running can be improved.

Further referring to FIG. 2 , as an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a device for generating vehicle route information, and these device embodiments correspond to those method embodiments shown in FIG. 1 , the The device can be specifically applied to various electronic devices.

As shown in FIG. 2 , the vehicle path information generation device 200 of some embodiments includes: an acquisition unit 201, a fusion processing unit 202, a first generation unit 203, a second generation unit 204, a detection processing unit 205, a third generation unit 206 and generating and sending unit 207. Wherein, the obtaining unit 201 is configured to obtain the current vehicle perception information, lane line perception information, the first obstacle feature information set, the second obstacle feature information set and the lane path information sequence; the fusion processing unit 202 is configured to be based on The above-mentioned lane line perception information is fused with the above-mentioned first obstacle feature information set and the above-mentioned second obstacle feature information set to obtain the current lane obstacle feature information set and the left lane obstacle feature information set; the first generating unit 203, configured to generate lane change expectation information based on the above-mentioned current vehicle perception information and the above-mentioned current lane obstacle feature information group; the second generation unit 204 is configured to change lanes in response to determining that the above-mentioned lane-change expectation information meets preset expectations condition, generating merging information based on the above-mentioned current vehicle perception information and the above-mentioned lane path information sequence; the detection processing unit 205 is configured to respond to determining that the above-mentioned merging information satisfies a preset distance condition, and analyze the above-mentioned left lane obstacle characteristic information The group performs detection processing to obtain lane-changing decision information; the third generation unit 206 is configured to respond to determining that the lane-changing decision information meets the preset lane-changing conditions, based on the preset high-precision map, the above-mentioned current vehicle perception information, the above-mentioned The left lane obstacle characteristic information group and the above-mentioned lane line perception information generate lane-changing trajectory information; the generating and sending unit 207 is configured to generate vehicle path information, and sending the above vehicle path information to the vehicle control module for controlling vehicle driving, wherein the generating of the vehicle path information includes: generating the merging trajectory information based on the above lane changing trajectory information and the above merging information; based on the above merging Lane information, intercepting the above-mentioned lane path information sequence to obtain the intercepted lane path information sequence; determining the above-mentioned lane change trajectory information, the above-mentioned merge trajectory information and the above-mentioned intercepted lane path information sequence as vehicle path information.

It can be understood that the units recorded in the device 200 correspond to the steps in the method described with reference to FIG. 1 . Therefore, the operations, features and beneficial effects described above for the method are also applicable to the device 200 and the units contained therein, and will not be repeated here.

Further referring to FIG. 3 , it shows a schematic structural diagram of an electronic device 300 suitable for implementing some embodiments of the present disclosure. The electronic device shown in FIG. 3 is only an example, and should not limit the functions and scope of use of the embodiments of the present disclosure.

As shown in FIG. 3 , an electronic device 300 may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 301 that can be randomly accessed according to a program stored in a read-only memory (ROM) 302 or loaded from a storage device 308 Various appropriate actions and processes are executed by programs in the memory (RAM) 303 . In the RAM 303, various programs and data necessary for the operation of the electronic device 300 are also stored. The processing device 301 , ROM 302 and RAM 303 are connected to each other through a bus 304 . An input/output (I/O) interface 305 is also connected to the bus 304 .

Typically, the following devices can be connected to the I/O interface 305: input devices 306 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; including, for example, a liquid crystal display (LCD), speaker, vibration an output device 307 such as a computer; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication means 309 may allow the electronic device 300 to perform wireless or wired communication with other devices to exchange data. While FIG. 3 shows electronic device 300 having various means, it should be understood that implementing or having all of the means shown is not a requirement. More or fewer means may alternatively be implemented or provided. Each block shown in FIG. 3 may represent one device, or may represent multiple devices as required.

In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, where the computer program includes program codes for executing the methods shown in the flowcharts. In some such embodiments, the computer program may be downloaded and installed from a network via communication means 309 , or from storage means 308 , or from ROM 302 . When the computer program is executed by the processing device 301, the above-mentioned functions defined in the methods of some embodiments of the present disclosure are performed.

It should be noted that the above-mentioned computer-readable medium in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, electrical connections with one or more wires, portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable Programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code therein. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can transmit, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device . Program code embodied on a computer readable medium may be transmitted by any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

In some embodiments, the client and the server can communicate using any currently known or future-developed network protocols such as HTTP (HyperText Transfer Protocol), and can communicate with digital data in any form or medium (eg, communication network) interconnections. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), internetworks (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network of.

The above-mentioned computer-readable medium may be included in the above-mentioned device, or may exist independently without being incorporated into the electronic device. The above-mentioned computer-readable medium carries one or more programs, and when the above-mentioned one or more programs are executed by the electronic device, the electronic device: acquires current vehicle perception information, lane line perception information, first obstacle feature information set , the second obstacle feature information set and the lane path information sequence; based on the above-mentioned lane line perception information, the above-mentioned first obstacle feature information set and the above-mentioned second obstacle feature information set are fused to obtain the current lane obstacle feature information group and the left lane obstacle characteristic information group; based on the above-mentioned current vehicle perception information and the above-mentioned current lane obstacle characteristic information group, generate lane change expectation information; in response to determining that the above lane change expectation information meets the preset expected lane change conditions, based on The above-mentioned current vehicle perception information and the above-mentioned lane path information sequence generate lane-merging information; in response to determining that the above-mentioned lane-merging information satisfies a preset distance condition, perform detection and processing on the above-mentioned left lane obstacle feature information group to obtain lane-changing decision information; In response to determining that the above-mentioned lane-changing decision information satisfies the preset lane-changing conditions, generate lane-changing track information based on the preset high-precision map, the above-mentioned current vehicle perception information, the above-mentioned left lane obstacle feature information group, and the above-mentioned lane line perception information ; Generate vehicle path information based on the above-mentioned lane-changing trajectory information, the above-mentioned lane-merging information, and the above-mentioned lane path information sequence, and send the above-mentioned vehicle path information to the vehicle control module for controlling vehicle driving, wherein the above-mentioned generated vehicle path information includes: Based on the above lane changing trajectory information and the above lane merging information, generate lane merge trajectory information; based on the above lane merge information, intercept the above lane path information sequence to obtain the intercepted lane path information sequence; combine the above lane change trajectory information, the above merge The track information and the above intercepted lane path information sequence are determined as vehicle path information.

Computer program code for carrying out operations of some embodiments of the present disclosure may be written in one or more programming languages, or combinations thereof, including object-oriented programming languages—such as Java, Smalltalk, C++, Also included are conventional procedural programming languages - such as the "C" language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In cases involving a remote computer, the remote computer can be connected to the user computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to connected via the Internet).

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, program segment, or portion of code that contains one or more logical functions for implementing specified executable instructions. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved. It should also be noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by a dedicated hardware-based system that performs the specified functions or operations , or may be implemented by a combination of dedicated hardware and computer instructions.

The units described in some embodiments of the present disclosure may be realized by software or by hardware. The described unit may also be set in a processor, for example, it may be described as: a processor includes an acquisition unit, a fusion processing unit, a first generation unit, a second generation unit, a detection processing unit, a third generation unit and a generation and sending unit. Wherein, the names of these units do not constitute a limitation of the unit itself in some cases, for example, the acquisition unit can also be described as "acquire current vehicle perception information, lane line perception information, first obstacle feature information set, A unit of the second obstacle feature information set and lane path information sequence".

The functions described herein above may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), System on Chips (SOCs), Complex Programmable Logical device (CPLD) and so on.

The above descriptions are only some preferred embodiments of the present disclosure and illustrations of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solution formed by the specific combination of the above-mentioned technical features, but also covers the above-mentioned invention without departing from the above-mentioned inventive concept. Other technical solutions formed by any combination of technical features or equivalent features. For example, a technical solution formed by replacing the above-mentioned features with technical features having similar functions disclosed in (but not limited to) the embodiments of the present disclosure.

Claims (6)

1. A method for generating vehicle route information, comprising: Obtain current vehicle perception information, lane line perception information, first obstacle feature information set, second obstacle feature information set, and lane path information sequence; Based on the lane line perception information, performing fusion processing on the first obstacle feature information set and the second obstacle feature information set to obtain the current lane obstacle feature information set and the left lane obstacle feature information set; generating lane change expectation information based on the current vehicle perception information and the current lane obstacle feature information group; In response to determining that the lane change expectation information satisfies a preset expected lane change condition, generating lane merging information based on the current vehicle perception information and the lane path information sequence; In response to determining that the lane merging information satisfies a preset distance condition, perform detection processing on the left lane obstacle feature information group to obtain lane change decision information; In response to determining that the lane change decision information satisfies a preset lane change condition, based on a preset high-precision map, the current vehicle perception information, the left lane obstacle characteristic information group, and the lane line perception information, generate Lane change trajectory information; Based on the lane change trajectory information, the lane merging information and the lane path information sequence, generate vehicle path information, and send the vehicle path information to a vehicle control module for controlling vehicle driving, wherein the generating vehicle Path information includes: generating merge trajectory information based on the lane change trajectory information and the merge information; Intercepting the lane path information sequence based on the merging information to obtain the intercepted lane path information sequence; determining the lane-changing trajectory information, the merging trajectory information, and the intercepted lane path information sequence as vehicle path information; Wherein, based on the lane line perception information, the first obstacle feature information set and the second obstacle feature information set are fused to obtain the current lane obstacle feature information set and the left lane obstacle Feature information group, including: performing deduplication processing on each first obstacle characteristic information set in the first obstacle characteristic information set, to obtain the first obstacle characteristic information set after deduplication; performing deduplication processing on each second obstacle feature information set in the second obstacle feature information set to obtain a deredundant second obstacle feature information set; For each de-redundancy first obstacle feature information in the de-redundancy first obstacle feature information set, combine the de-redundancy second obstacle feature information with the de-redundancy first obstacle feature The second obstacle feature information after deduplication matching the information is determined as the obstacle feature information to be deleted; Deleting the deredundant second obstacle feature information set that matches the obtained obstacle feature information to be deleted from the deredundancy second obstacle feature information set to obtain the deleted second obstacle feature information set; generating a target obstacle feature information set based on the first obstacle feature information set after deduplication and the deleted second obstacle feature information set; Based on the lane line perception information, the target obstacle feature information set is classified to obtain a current lane obstacle feature information set and a left lane obstacle feature information set. 2. The method according to claim 1, wherein the current vehicle perception information includes a vehicle speed value, and each current lane obstacle characteristic information in the current lane obstacle characteristic information group includes a longitudinal distance value; and The generating lane change expectation information based on the current vehicle perception information and the current lane obstacle feature information group includes: From the current lane obstacle feature information group, select the current lane obstacle feature information that satisfies the preset forward orientation condition as the front obstacle vehicle feature information, and obtain the front obstacle vehicle feature information group; Selecting the vehicle characteristic information of the obstacle ahead satisfying the preset longitudinal distance condition from the vehicle characteristic information group of the obstacle ahead as the vehicle characteristic information of the target obstacle; Acquiring a speed threshold corresponding to the longitudinal distance value included in the vehicle characteristic information of the target obstacle; In response to determining that the vehicle speed value included in the current vehicle perception information is less than or equal to the speed threshold, determining whether a preset expected speed value is greater than the speed threshold; In response to determining that the preset expected speed value is greater than the speed threshold, the preset expected speed value and the preset left lane change sign are determined as lane change expectation information. 3. The method of claim 2, wherein the method further comprises: In response to determining that the preset expected speed value is less than or equal to the speed threshold, the speed threshold and the preset no-lane-change flag are determined as lane-change expectation information. 4. A device for generating vehicle route information, comprising: An acquisition unit configured to acquire current vehicle perception information, lane line perception information, a first set of obstacle feature information, a second set of obstacle feature information, and a sequence of lane path information; The fusion processing unit is configured to perform fusion processing on the first obstacle feature information set and the second obstacle feature information set based on the lane line perception information, to obtain the current lane obstacle feature information set and the left Lane obstacle feature information group; A first generation unit configured to generate lane change expectation information based on the current vehicle perception information and the current lane obstacle feature information group; A second generation unit configured to generate lane merging information based on the current vehicle perception information and the lane path information sequence in response to determining that the lane change expectation information meets a preset expected lane change condition; The detection processing unit is configured to, in response to determining that the merging information satisfies a preset distance condition, perform detection processing on the left lane obstacle feature information group to obtain lane change decision information; The third generation unit is configured to respond to determining that the lane change decision information satisfies a preset lane change condition, based on a preset high-precision map, the current vehicle perception information, the left lane obstacle characteristic information group and The lane line perception information generates lane change trajectory information; A generating and sending unit configured to generate vehicle path information based on the lane change trajectory information, the merging information and the lane path information sequence, and send the vehicle path information to a vehicle control module for controlling the vehicle Driving, wherein the generating vehicle route information includes: generating merge trajectory information based on the lane change trajectory information and the merge information; Intercepting the lane path information sequence based on the merging information to obtain the intercepted lane path information sequence; determining the lane-changing trajectory information, the merging trajectory information, and the intercepted lane path information sequence as vehicle path information; Wherein, based on the lane line perception information, the first obstacle feature information set and the second obstacle feature information set are fused to obtain the current lane obstacle feature information set and the left lane obstacle Feature information group, including: performing deduplication processing on each first obstacle characteristic information set in the first obstacle characteristic information set, to obtain the first obstacle characteristic information set after deduplication; performing deduplication processing on each second obstacle feature information set in the second obstacle feature information set to obtain a deredundant second obstacle feature information set; For each de-redundancy first obstacle feature information in the de-redundancy first obstacle feature information set, combine the de-redundancy second obstacle feature information with the de-redundancy first obstacle feature The second obstacle feature information after deduplication matching the information is determined as the obstacle feature information to be deleted; Deleting the deredundant second obstacle feature information set that matches the obtained obstacle feature information to be deleted from the deredundancy second obstacle feature information set to obtain the deleted second obstacle feature information set; generating a target obstacle feature information set based on the first obstacle feature information set after deduplication and the deleted second obstacle feature information set; Based on the lane line perception information, the target obstacle feature information set is classified to obtain a current lane obstacle feature information set and a left lane obstacle feature information set. 5. An electronic device comprising: one or more processors; a storage device on which one or more programs are stored, When the one or more programs are executed by the one or more processors, the one or more processors are made to implement the method according to any one of claims 1-3. 6. A computer-readable medium, on which a computer program is stored, wherein, when the computer program is executed by a processor, the method according to any one of claims 1-3 is realized.

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