Trajectory planning method, device and equipment for autonomous vehicle and storage medium

Abstract

The invention discloses a trajectory planning method, device and equipment for an autonomous vehicle, and a storage medium, and relates to the technical field of computers. The method comprises the following steps: acquiring driving data of an automatic driving vehicle, driving data of a retrograde obstacle and road data; based on the driving data of the autonomous vehicle, the driving data of the retrograde obstacles and the road data, performing classification processing on the retrograde obstacles by using a preset scene classification strategy; in response to the condition that the retrograde obstacle is a first scene retrograde obstacle, based on the driving data of the autonomous vehicle and the driving data of the first scene retrograde obstacle, utilizing a first planning model to obtain a first planning track of the autonomous vehicle; and in response to the condition that the retrograde obstacle is a second scene retrograde obstacle, based on the driving data of the autonomous vehicle and the driving data of the second scene retrograde obstacle, utilizing a second planning model to obtain a second planning track of the autonomous vehicle.

Description

Technical Field

[0001] The present application relates to the field of computer technology, specifically to technical fields such as intelligent transportation and autonomous driving, and in particular to a trajectory planning method, device, equipment and storage medium for an autonomous driving vehicle. Background Art

[0002] Typically, in driving scenarios filled with a large number of reverse obstacles, such as unstructured roads or non-motorized vehicle lanes, due to the complex driving environment, autonomous driving vehicles need to effectively deal with reverse obstacles to ensure vehicle driving safety.

[0003] At present, most of the technical solutions for path planning of autonomous vehicles for reverse obstacles use the SL-ST iterative optimization method to bypass reverse obstacles, which can effectively solve the interaction problem of reverse obstacles in most scenarios. However, in the driving scenario with narrow road sections, the lateral avoidance space is limited, and the longitudinal speed planning is more dependent. The above-mentioned related technical solutions are relatively conservative in this scenario, and the processing process is less smooth and interactive. Summary of the invention

[0004] The present application provides a trajectory planning method, device, equipment and storage medium for an autonomous driving vehicle, which optimizes the reliability of the planned trajectory of the vehicle in dealing with retrograde obstacles. The technical solution is as follows: In a first aspect, a trajectory planning method for an autonomous driving vehicle is provided, the method comprising: Acquire driving data of the autonomous vehicle, driving data of the reverse obstacle, and road data; Based on the driving data of the autonomous driving vehicle, the driving data of the wrong-traffic obstacle, and the road data, the wrong-traffic obstacle is classified and processed using a preset scene classification strategy; In response to the reverse obstacle being a first-scenario reverse obstacle, obtaining a first planned trajectory of the autonomous driving vehicle using a first planning model based on the driving data of the autonomous driving vehicle and the driving data of the first-scenario reverse obstacle; In response to the reverse obstacle being a second-scenario reverse obstacle, a second planned trajectory of the autonomous driving vehicle is obtained using a second planning model based on the driving data of the autonomous driving vehicle and the driving data of the second-scenario reverse obstacle.

[0005] In a possible implementation, the obtaining, based on the driving data of the autonomous driving vehicle and the driving data of the reverse obstacle in the first scene, of a first planning model, a first planning trajectory of the autonomous driving vehicle includes: Determining a motion vector of the autonomous driving vehicle using a first planning model based on the driving data of the autonomous driving vehicle and the driving data of the reverse obstacle in the first scene; Based on the driving data of the autonomous driving vehicle and the motion vector, a first planned trajectory of the autonomous driving vehicle is obtained.

[0006] In a possible implementation, determining the motion vector of the autonomous driving vehicle using a first planning model based on the driving data of the autonomous driving vehicle and the driving data of the reverse obstacle in the first scene includes: Determining a state vector of the autonomous driving vehicle based on the driving data of the autonomous driving vehicle; Inferring a state vector of the first-scenario reverse-traffic obstacle based on the driving data of the autonomous driving vehicle and the driving data of the first-scenario reverse-traffic obstacle; Optimizing the objective function of the first planning model based on the state vector of the autonomous driving vehicle, the state vector of the reverse obstacle in the first scene, and the constraint conditions of the first planning model; Based on the result of the optimization process, an action vector of the autonomous driving vehicle is determined.

[0007] In a possible implementation, the driving data of the second-scenario wrong-way obstacle includes a first predicted trajectory and a second predicted trajectory, and obtaining the second planned trajectory of the autonomous driving vehicle by using a second planning model based on the driving data of the autonomous driving vehicle and the driving data of the second-scenario wrong-way obstacle includes: Determining an action vector of the autonomous driving vehicle using a second planning model based on the driving data of the autonomous driving vehicle, the first predicted trajectory, and the second predicted trajectory; Based on the driving data of the autonomous driving vehicle and the motion vector, a second planned trajectory of the autonomous driving vehicle is obtained.

[0008] In a possible implementation, determining the action vector of the autonomous driving vehicle by using a second planning model based on the driving data of the autonomous driving vehicle, the first predicted trajectory, and the second predicted trajectory includes: Determining a state vector of the autonomous driving vehicle based on the driving data of the autonomous driving vehicle; determining a state vector of the first predicted trajectory based on the first predicted trajectory of the second scenario retrograde obstacle; Determining a state vector of the second predicted trajectory based on the second predicted trajectory of the retrograde obstacle in the second scenario; Optimizing the objective function of the second planning model based on the state vector of the autonomous driving vehicle, the state vector of the first predicted trajectory, the state vector of the second predicted trajectory, and the constraints of the second planning model; Based on the result of the optimization process, an action vector of the autonomous driving vehicle is determined.

[0009] In a possible implementation, the classifying process of the reverse traffic obstacle based on the driving data of the autonomous driving vehicle, the driving data of the reverse traffic obstacle, and the road data using a preset scene classification strategy includes: Determining whether the driving data of the reverse obstacle matches the driving data of the autonomous driving vehicle; In response to the driving data of the wrong-traffic obstacle matching the driving data of the autonomous driving vehicle, determining that the wrong-traffic obstacle is a first-scenario wrong-traffic obstacle; In response to the driving data of the wrong-travel obstacle not matching the driving data of the autonomous driving vehicle, it is determined that the wrong-travel obstacle is a second-scenario wrong-travel obstacle.

[0010] In a possible implementation, the classifying the reverse traffic obstacle based on the driving data of the autonomous driving vehicle, the driving data of the reverse traffic obstacle, and the road data using a preset scene classification strategy further includes: Determining a lane area corresponding to the autonomous driving vehicle based on the driving data and road data of the autonomous driving vehicle; Determining whether the driving data of the oncoming obstacle matches the lane area corresponding to the autonomous driving vehicle; In response to the driving data of the wrong-travel obstacle matching the lane area corresponding to the autonomous driving vehicle, determining that the wrong-travel obstacle is a first-scenario wrong-travel obstacle; In response to the driving data of the wrong-way obstacle not matching the lane area corresponding to the autonomous driving vehicle, the wrong-way obstacle is determined to be a second-scenario wrong-way obstacle.

[0011] In a possible implementation, after obtaining the driving data of the autonomous driving vehicle, the driving data of the reverse obstacle, and the road data, the method further includes: Based on the driving data of the autonomous driving vehicle and the driving data of the reverse obstacle, a safety distance is calculated using a safety detection algorithm; Based on the driving data of the autonomous driving vehicle and the driving data of the reverse obstacle, a relative distance between the autonomous driving vehicle and the reverse obstacle is calculated; When the relative distance and the safety distance meet preset conditions, the braking acceleration is calculated based on the safety distance, the driving data of the autonomous driving vehicle and the driving data of the reverse obstacle to control the driving of the autonomous driving vehicle based on the braking acceleration.

[0012] In a second aspect, a trajectory planning device for an autonomous driving vehicle is provided, the device comprising: A first acquisition unit, used to acquire driving data of the autonomous driving vehicle, driving data of a reverse obstacle, and road data; A first classification unit is used to classify the reverse traffic obstacle based on the driving data of the autonomous driving vehicle, the driving data of the reverse traffic obstacle, and the road data, using a preset scene classification strategy; a first planning unit, configured to obtain a first planned trajectory of the autonomous driving vehicle by using a first planning model based on the driving data of the autonomous driving vehicle and the driving data of the first scenario reverse traffic obstacle in response to the reverse traffic obstacle being a first scenario reverse traffic obstacle; The second planning unit is used to obtain a second planned trajectory of the autonomous driving vehicle by using a second planning model based on the driving data of the autonomous driving vehicle and the driving data of the second-scenario reverse traffic obstacle in response to the reverse traffic obstacle being a second-scenario reverse traffic obstacle.

[0013] In a third aspect, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement the method of the above-mentioned aspect and any possible implementation manner.

[0014] In a fourth aspect, an electronic device is provided, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any possible implementation manner and the aspects described above.

[0015] According to a fifth aspect, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the above-mentioned aspects and any possible implementation method.

[0016] In a sixth aspect, an autonomous driving vehicle is provided, comprising the electronic device as described above.

[0017] The beneficial effects of the technical solution provided by this application include at least: It can be seen from the above technical scheme that the embodiment of the present application can obtain the driving data of the autonomous driving vehicle, the driving data of the reverse obstacle, and the road data, and then based on the driving data of the autonomous driving vehicle, the driving data of the reverse obstacle, and the road data, the reverse obstacle can be classified and processed by using a preset scene classification strategy; in response to the reverse obstacle being a first-scene reverse obstacle, based on the driving data of the autonomous driving vehicle and the driving data of the first-scene reverse obstacle, a first planning trajectory of the autonomous driving vehicle is obtained by using a first planning model; in response to the reverse obstacle being a second-scene reverse obstacle, based on the driving data of the autonomous driving vehicle and the driving data of the first-scene reverse obstacle, a first planning trajectory of the autonomous driving vehicle is obtained; The second planning model is used to obtain the second planning trajectory of the autonomous driving vehicle based on the driving data of the reverse obstacles and the driving data of the second scenario's reverse obstacles. Since the reverse obstacles can be classified into scenarios first, the autonomous driving vehicle can then plan trajectories for the reverse obstacles in different types of scenarios respectively, taking into account the limited rational interactive game of reverse obstacles, it can effectively solve the problem of narrow road passage of autonomous driving vehicles while avoiding emergency braking and evasive behavior caused by overly conservative trajectory planning of autonomous driving vehicles, taking into account the robustness and exploratory nature of the trajectory planning of autonomous driving vehicles, and improving the continuity of the planned trajectory of autonomous driving vehicles, thereby ensuring the reliability of autonomous driving vehicles.

[0018] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 It is a flowchart of a trajectory planning method for an autonomous driving vehicle provided by an embodiment of the present application; Figure 2 is a flow chart of a trajectory planning method for an autonomous driving vehicle provided by another embodiment of the present application; Figure 3 is a schematic diagram of a first scenario in a trajectory planning method for an autonomous driving vehicle provided in another embodiment of the present application; Figure 4 is a schematic diagram of a second scenario in a trajectory planning method for an autonomous driving vehicle provided in another embodiment of the present application; Figure 5A and5B to Fig. 9A and 9B is a schematic diagram of a planned trajectory and speed points for coping with a reverse obstacle in a first scenario of a trajectory planning method for an autonomous driving vehicle provided by another embodiment of the present application; Fig. 10A and 10B to Fig.14A and 14B is a schematic diagram of a planning result of a trajectory planning method for an autonomous driving vehicle to cope with a reverse obstacle in a second scenario provided by another embodiment of the present application; Fig.15 is a structural block diagram of a trajectory planning device for an autonomous driving vehicle provided in yet another embodiment of the present application; Fig.16 It is a block diagram of an electronic device used to implement the trajectory planning method of the autonomous driving vehicle of an embodiment of the present application. DETAILED DESCRIPTION

[0021] The following is a description of exemplary embodiments of the present application in conjunction with the accompanying drawings, including various details of the embodiments of the present application to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0022] Obviously, the described embodiments are only part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0023] It should be noted that the terminal devices involved in the embodiments of the present application may include but are not limited to mobile phones, personal digital assistants (PDAs), wireless handheld devices, tablet computers and other smart devices; display devices may include but are not limited to personal computers, televisions and other devices with display functions.

[0024] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0025] Usually, in the actual operation of autonomous driving, the driving environment is relatively complex. For example, the terminal delivery scenes of autonomous driving vehicles are mostly located on unstructured roads or non-motorized vehicle lanes. This type of scene is filled with a large number of reverse obstacles, such as delivery personnel, non-motorized vehicles, etc.

[0026] For the handling of reverse obstacles, most of the related technical methods are inherited or optimized from some open source solutions. For example, the SL-ST iterative optimization method is used to bypass reverse obstacles, which can better solve the interaction problem of reverse obstacles in most scenarios. However, the delivery scenes of autonomous driving vehicles often have narrow sections, limited lateral avoidance space, and greater reliance on longitudinal speed planning, but the ST graph search method is more conservative in this scenario, and the processing process is less smooth and interactive.

[0027] Therefore, there is an urgent need for a trajectory planning method for autonomous driving vehicles that can enable effective interaction between the vehicle and retrograde obstacles, thereby improving the continuity and reliability of the vehicle's planned trajectory.

[0028] Please refer to Figure 1 , which shows a flow chart of a trajectory planning method for an autonomous driving vehicle provided by an embodiment of the present application. The trajectory planning method for an autonomous driving vehicle may specifically include: Step 101: Acquire driving data of the autonomous driving vehicle, driving data of the reverse obstacle, and road data.

[0029] Step 102: Based on the driving data of the autonomous driving vehicle, the driving data of the wrong-traffic obstacle, and the road data, the wrong-traffic obstacle is classified using a preset scene classification strategy.

[0030] Step 103: In response to the reverse obstacle being a first-scenario reverse obstacle, based on the driving data of the autonomous driving vehicle and the driving data of the first-scenario reverse obstacle, a first planning trajectory of the autonomous driving vehicle is obtained using a first planning model.

[0031] Step 104: In response to the reverse obstacle being a second-scenario reverse obstacle, based on the driving data of the autonomous driving vehicle and the driving data of the second-scenario reverse obstacle, a second planned trajectory of the autonomous driving vehicle is obtained using a second planning model.

[0032] At this point, the driving of the autonomous driving vehicle can be controlled based on the first planned trajectory of the autonomous driving vehicle, or the second planned trajectory of the autonomous driving vehicle.

[0033] It should be noted that, here, the autonomous driving vehicle is the ego vehicle. The driving data of the reverse obstacle may include driving state data such as the position and speed of the reverse obstacle perceived by the ego vehicle, and the predicted trajectory of the reverse obstacle obtained by the prediction module of the autonomous driving vehicle based on the driving state data.

[0034] It should be noted that the road data may be data of the current road area of ​​the autonomous driving vehicle. The road data may include but is not limited to lane markings, lane lines, lane width, etc.

[0035] It should be noted that the first scenario reverse obstacle may be a reverse obstacle on the planned path of the autonomous driving vehicle, and the second scenario reverse obstacle may be a reverse obstacle that may conflict with the autonomous driving vehicle in the future.

[0036] It should be noted that part or all of the execution subject of steps 101 to 104 may be an application located in the local terminal, or may also be a functional unit such as a plug-in or software development kit (SDK) set in the application located in the local terminal, or may also be a processing engine located in a network-side server, or may also be a distributed system located on the network side, for example, a processing engine or distributed system in an autonomous driving platform on the network side, etc. This embodiment does not specifically limit this.

[0037] It is understandable that the application may be a local program (nativeApp) installed on the local terminal, or may also be a webpage program (webApp) of a browser on the local terminal, which is not limited in this embodiment.

[0038] In this way, the scenarios of retrograde obstacles can be classified first, and the autonomous driving vehicle can then plan trajectories for retrograde obstacles in different types of scenarios respectively. Taking into account the limited rational interactive game of retrograde obstacles, the problem of narrow road passage of autonomous driving vehicles can be effectively solved, while avoiding emergency braking and evasive behavior caused by overly conservative trajectory planning of autonomous driving vehicles. The robustness and exploratory nature of the trajectory planning of autonomous driving vehicles are taken into account, and the continuity of the trajectory of autonomous driving vehicles is improved, thereby ensuring the reliability of autonomous driving vehicles.

[0039] Optionally, in a possible implementation of this embodiment, in step 103, first, based on the driving data of the autonomous driving vehicle and the driving data of the first scene reverse obstacle, the motion vector of the autonomous driving vehicle is determined using the first planning model. Secondly, based on the driving data of the autonomous driving vehicle and the motion vector, the first planning trajectory of the autonomous driving vehicle can be obtained.

[0040] In this implementation, the motion vector of the autonomous driving vehicle may be the acceleration corresponding to the trajectory point planned by the autonomous driving vehicle.

[0041] In a specific implementation process of this implementation method, first, the state vector of the autonomous driving vehicle can be determined based on the driving data of the autonomous driving vehicle. Secondly, the state vector of the first scenario retrograde obstacle can be inferred based on the driving data of the autonomous driving vehicle and the driving data of the first scenario retrograde obstacle. Thirdly, the objective function of the first planning model can be optimized based on the state vector of the autonomous driving vehicle, the state vector of the first scenario retrograde obstacle, and the constraints of the first planning model. Thirdly, the action vector of the autonomous driving vehicle can be determined based on the result of the optimization process.

[0042] In this implementation, the driving data of the autonomous driving vehicle may include the posture, speed, planned path, etc. of the autonomous driving vehicle. The posture of the autonomous driving vehicle includes position and orientation. The driving data of the reverse obstacle may include the posture, speed, etc. of the reverse obstacle perceived by the vehicle. The posture of the reverse obstacle includes position and orientation.

[0043] One scenario of this specific implementation process is that the position, speed and planned path of the autonomous driving vehicle can be used to determine the position, speed and acceleration of each position point on the planned path of the autonomous driving vehicle, and the position, speed and acceleration of each position point on the planned path of the autonomous driving vehicle can be used as the state vector of the autonomous driving vehicle.

[0044] Another situation of this specific implementation process is that based on the position of the autonomous driving vehicle, the planned path and the position of the retrograde obstacle in the first scene, the movement path of the retrograde obstacle in the first scene is inferred, and then based on the movement path of the retrograde obstacle in the first scene, the position, speed and acceleration of each position point on the movement path of the retrograde obstacle in the first scene can be determined, and the position, speed and acceleration of each position point on the movement path are used as the state vector of the retrograde obstacle in the first scene.

[0045] In the specific implementation process, based on the current position of the autonomous driving vehicle and the current position of the retrograde obstacle in the first scenario, the movement path of the retrograde obstacle in the first scenario is reversely deduced according to the planned path of the autonomous driving vehicle.

[0046] In this implementation, the first planning model may be a model predictive control game (MPCG) model.

[0047] Another situation of the specific implementation process is that first, the objective function of the first planning model can be optimized and solved based on the state vector of the autonomous driving vehicle, the state vector of the first scene reverse obstacle, and the constraint conditions of the model predictive control game model. Then, the action vector of the autonomous driving vehicle can be obtained based on the result of the optimization solution.

[0048] Another situation of the specific implementation process is that, first, the probability of the first scene of the current frame taking acceleration or deceleration can be obtained, and the objective function of the first planning model can be optimized and solved based on the state vector of the autonomous driving vehicle, the probability of the first scene of the reverse obstacle taking acceleration or deceleration, the state vector of the first scene of the reverse obstacle, and the constraint conditions of the model predictive control game model. Thirdly, the action vector of the autonomous driving vehicle can be obtained based on the result of the optimization solution.

[0049] Exemplarily, the objective function and constraints of the first planning model may be as shown in formula (7).

[0050] In another specific implementation process of this implementation method, the planned trajectory, position, speed and action vector of the autonomous driving vehicle may be fused to obtain a first planned trajectory for the autonomous driving vehicle to cope with a retrograde obstacle in a first scenario.

[0051] In this way, when it is determined that the reverse obstacle that the autonomous driving vehicle is dealing with is the obstacle of the first scene, the action vector of the autonomous driving vehicle dealing with the reverse obstacle of the first scene can be calculated by utilizing the first planning model based on the driving data of the autonomous driving vehicle and the driving data of the reverse obstacle of the first scene, and the driving data and action vector of the autonomous driving vehicle can be used to obtain a more accurate first planning trajectory of the autonomous driving vehicle dealing with the reverse obstacle of the first scene, thereby further improving the continuity of the planned trajectory of the autonomous driving vehicle and ensuring the reliability of the driving of the autonomous driving vehicle.

[0052] It should be noted that the various specific implementation processes provided in this implementation can be combined with each other to implement the steps in the trajectory planning method for the autonomous driving vehicle of this embodiment. For a detailed description, please refer to the relevant content in this implementation, which will not be repeated here.

[0053] Optionally, in a possible implementation of this embodiment, the driving data of the second scene reverse obstacle may include a first predicted trajectory and a second predicted trajectory. In step 104, first, based on the driving data of the autonomous driving vehicle, the first predicted trajectory and the second predicted trajectory, a second planning model may be used to determine the motion vector of the autonomous driving vehicle. Secondly, based on the driving data of the autonomous driving vehicle and the motion vector, a second planned trajectory of the autonomous driving vehicle may be obtained.

[0054] In this implementation, at least one of the first predicted trajectory and the second predicted trajectory of the second scene retrograde obstacle conflicts with the planned path of the autonomous driving vehicle.

[0055] In this implementation, the second planning model may be a contigency model predictive control model (CMPC).

[0056] In a specific implementation process of this implementation method, first, the state vector of the autonomous driving vehicle can be determined based on the driving data of the autonomous driving vehicle. Secondly, the state vector of the first predicted trajectory can be determined based on the first predicted trajectory of the retrograde obstacle in the second scene. Thirdly, the state vector of the second predicted trajectory can be determined based on the second predicted trajectory of the retrograde obstacle in the second scene. Thirdly, the objective function of the second planning model can be optimized based on the state vector of the autonomous driving vehicle, the state vector of the first predicted trajectory, the state vector of the second predicted trajectory, and the constraints of the second planning model. Thirdly, based on the result of the optimization process, the action vector of the autonomous driving vehicle is determined.

[0057] In this implementation, the state vector of the autonomous driving vehicle may include the position, velocity, and acceleration of each position point on the planned path of the autonomous driving vehicle. The state vector of the first predicted trajectory may include the position, velocity, and acceleration of each trajectory point on the first predicted trajectory. The state vector of the second predicted trajectory may include the position, velocity, and acceleration of each trajectory point on the second predicted trajectory.

[0058] One situation of the specific implementation process is that, first, the probability of the first predicted trajectory of the current frame conflicting with the planned path of the autonomous driving vehicle can be obtained, and the objective function of the second planning model can be optimized based on the state vector of the autonomous driving vehicle, the probability of the first predicted trajectory of the current frame conflicting with the planned path of the autonomous driving vehicle, the state vector of the first predicted trajectory, the state vector of the second predicted trajectory, and the constraint conditions of the second planning model. Thirdly, based on the result of the optimization process, the action vector of the autonomous driving vehicle is determined.

[0059] Exemplarily, the objective function and constraints of the second planning model may be as shown in formula (11).

[0060] In another specific implementation process of this implementation method, it can be determined whether the position relationship between the autonomous driving vehicle and the position relationship of the retrograde obstacle in the second scene meets the preset relationship conditions. If the position relationship between the autonomous driving vehicle and the retrograde obstacle in the second scene meets the preset relationship conditions, the motion vector of the autonomous driving vehicle can be determined based on the driving data of the autonomous driving vehicle, the first predicted trajectory and the second predicted trajectory using a second planning model, and then the second planned trajectory of the autonomous driving vehicle can be obtained based on the driving data of the autonomous driving vehicle and the motion vector.

[0061] Here, the preset relationship condition may be that the second scenario reverse traffic obstacle is located in front of the autonomous driving vehicle. For example, when the difference between the position of the autonomous driving vehicle and the position of the second scenario reverse traffic obstacle is less than zero, it may be indicated that the second scenario reverse traffic obstacle is located in front of the autonomous driving vehicle.

[0062] In this way, when it is determined that the reverse obstacle that the autonomous driving vehicle is dealing with is an obstacle in the second scenario, the second planning model can be used to calculate the action vector of the autonomous driving vehicle in dealing with the reverse obstacle in the second scenario based on the driving data of the autonomous driving vehicle and the first predicted trajectory and the second predicted trajectory of the reverse obstacle in the second scenario. The driving data and the action vector of the autonomous driving vehicle can be used to obtain a more accurate second planning trajectory of the autonomous driving vehicle in dealing with the reverse obstacle in the second scenario, thereby further improving the continuity of the planned trajectory of the autonomous driving vehicle in this scenario, thereby ensuring the reliability of the driving of the autonomous driving vehicle.

[0063] It should be noted that the various specific implementation processes provided in this implementation can be combined with the aforementioned implementation to implement the steps in the trajectory planning method for the autonomous driving vehicle of this embodiment. For a detailed description, please refer to the relevant content in this implementation, which will not be repeated here.

[0064] Optionally, in a possible implementation of this embodiment, in step 102, first, it may be determined whether the driving data of the reverse traffic obstacle matches the driving data of the autonomous driving vehicle. Secondly, in response to the driving data of the reverse traffic obstacle matching the driving data of the autonomous driving vehicle, it may be determined that the reverse traffic obstacle is a reverse traffic obstacle of the first scenario. Thirdly, in response to the driving data of the reverse traffic obstacle not matching the driving data of the autonomous driving vehicle, it may be determined that the reverse traffic obstacle is a reverse traffic obstacle of the second scenario.

[0065] In a specific implementation process of this implementation method, first, the position of the retrograde obstacle and the planned path of the autonomous driving vehicle can be matched. When the position of the retrograde obstacle is on the planned path of the autonomous driving vehicle, the retrograde obstacle can be classified as a first-scenario retrograde obstacle; when the position of the retrograde obstacle is not on the planned path of the autonomous driving vehicle, the retrograde obstacle can be classified as a second-scenario retrograde obstacle.

[0066] It is understandable that, here, the position of the reverse obstacle may be the current position of the reverse obstacle in the current planning frame. The planned path of the autonomous driving vehicle may be the current planned path of the autonomous driving vehicle in the current planning frame.

[0067] Optionally, in a possible implementation of this embodiment, in step 102, further, based on the driving data and road data of the autonomous driving vehicle, the lane area corresponding to the autonomous driving vehicle can be determined, and then it can be determined whether the driving data of the reverse obstacle matches the lane area corresponding to the autonomous driving vehicle. In response to the driving data of the reverse obstacle matching the lane area corresponding to the autonomous driving vehicle, the reverse obstacle is determined to be a first-scenario reverse obstacle. In response to the driving data of the reverse obstacle not matching the lane area corresponding to the autonomous driving vehicle, the reverse obstacle is determined to be a second-scenario reverse obstacle.

[0068] In a specific implementation process of this implementation method, first, the lane area of ​​the lane where the autonomous driving vehicle is located can be determined based on the position and planned path of the autonomous driving vehicle, the lane lines and lane widths in the road data. Secondly, the position of the reverse obstacle can be matched with the lane area corresponding to the autonomous driving vehicle. Thirdly, when the position of the reverse obstacle is located in the lane area corresponding to the autonomous driving vehicle, the reverse obstacle can be classified as a first-scenario reverse obstacle; when the position of the reverse obstacle is not located in the lane area corresponding to the autonomous driving vehicle, the reverse obstacle can be classified as a second-scenario reverse obstacle.

[0069] In another specific implementation process of this implementation method, the first-scenario retrograde obstacles determined by the above two implementation methods can be combined to obtain all the first-scenario retrograde obstacles perceived by the autonomous driving vehicle. The second-scenario retrograde obstacles determined by the above two implementation methods can also be combined to obtain all the second-scenario retrograde obstacles perceived by the autonomous driving vehicle.

[0070] In this way, by classifying the driving scenarios of retrograde obstacles, it is possible to plan the trajectory of the autonomous driving vehicle to deal with retrograde obstacles of different scene types, which can further improve the continuity of the planned trajectory and thus improve the driving reliability of the autonomous driving vehicle.

[0071] It should be noted that the various specific implementation processes provided in this implementation can be combined with the aforementioned implementation to implement the steps in the trajectory planning method for the autonomous driving vehicle of this embodiment. For a detailed description, please refer to the relevant content in this implementation, which will not be repeated here.

[0072] Optionally, in a possible implementation of this embodiment, after step 101, first, a safety distance may be calculated based on the driving data of the autonomous driving vehicle and the driving data of the reverse obstacle using a safety detection algorithm. Secondly, a relative distance between the autonomous driving vehicle and the reverse obstacle may be calculated based on the driving data of the autonomous driving vehicle and the driving data of the reverse obstacle. Thirdly, when the relative distance and the safety distance meet preset conditions, a braking acceleration may be calculated based on the safety distance, the driving data of the autonomous driving vehicle and the driving data of the reverse obstacle to control the driving of the autonomous driving vehicle based on the braking acceleration.

[0073] In this implementation, the preset condition may include that the relative distance is less than the safety distance.

[0074] In another specific implementation process of this implementation, first, the relative distance between the autonomous driving vehicle and the reverse obstacle can be calculated based on the position of the autonomous driving vehicle and the position of the reverse obstacle. Again, when it is determined that the relative distance is less than the safe distance, the braking acceleration is calculated based on the safe distance, the speed of the autonomous driving vehicle, and the speed of the reverse obstacle. Again, the driving of the autonomous driving vehicle can be controlled based on the braking acceleration. Again, when it is determined that the relative distance is not less than the safe distance, step 102 can be continued.

[0075] It is understandable that before planning the trajectory of an autonomous vehicle, it is possible to first determine whether the current driving situation of the autonomous vehicle is a dangerous driving situation, that is, to determine whether the distance between the autonomous vehicle and the oncoming obstacle meets the safety distance. If it is determined that the autonomous vehicle is in a dangerous driving situation, the braking acceleration can be directly calculated, and the autonomous vehicle can be braked based on the braking acceleration.

[0076] In addition, after planning the trajectory of the autonomous driving vehicle, it is also possible to determine whether the current driving situation of the autonomous driving vehicle is a dangerous driving situation, that is, to determine whether the distance between the autonomous driving vehicle and the reverse obstacle meets the safety distance. If it is determined that the autonomous driving vehicle is in a dangerous driving situation, the braking acceleration can be directly calculated, and the autonomous driving vehicle can be braked based on the braking acceleration. If it is determined that the autonomous driving vehicle is in a safe driving situation, the driving of the autonomous driving vehicle can be controlled based on the first planned trajectory or the second planned trajectory.

[0077] In this way, the driving safety of the autonomous driving vehicle can be further guaranteed by performing safety detection on the autonomous driving vehicle.

[0078] It should be noted that the various specific implementation processes provided in this implementation can be combined with the aforementioned implementation to implement the steps in the trajectory planning method for the autonomous driving vehicle of this embodiment. For a detailed description, please refer to the relevant content in this implementation, which will not be repeated here.

[0079] In order to better understand the method of the embodiment of the present application, the method of the embodiment of the present application is described below in conjunction with the accompanying drawings and specific application scenarios.

[0080] Figure 2 is a flow chart of a trajectory planning method for an autonomous driving vehicle provided by another embodiment of the present application, such as Figure 2 shown.

[0081] Step 201: Acquire driving data of the autonomous driving vehicle, driving data of the reverse obstacle, and road data.

[0082] In this embodiment, the driving data of the autonomous driving vehicle may include but is not limited to posture data, speed, planned path, etc. The posture data of the autonomous driving vehicle may include position data and orientation data.

[0083] In this embodiment, the driving data of the reverse obstacle information may include data sensed by the autonomous driving vehicle and related data obtained after the autonomous driving vehicle processes the sensed data. The driving data of the reverse obstacle information may include but is not limited to posture data, speed, predicted trajectory, etc. The predicted trajectory may be a multi-modal predicted trajectory, that is, the number of predicted trajectories may be multiple.

[0084] In this embodiment, the road data may include but is not limited to lane lines, lane markings, lane width, etc.

[0085] Step 202: Based on the driving data of the autonomous driving vehicle, the driving data of the wrong-traffic obstacle, and the road data, the wrong-traffic obstacle is classified using a preset scene classification strategy.

[0086] In this embodiment, it is determined whether the driving data of the reverse obstacle matches the driving data of the autonomous driving vehicle. If the driving data of the reverse obstacle matches the driving data of the autonomous driving vehicle, the reverse obstacle is classified as a first-scenario reverse obstacle; if the driving data of the reverse obstacle does not match the driving data of the autonomous driving vehicle, the reverse obstacle is classified as a second-scenario reverse obstacle.

[0087] Preferably, the position data of the retrograde obstacle and the planned path of the autonomous driving vehicle are matched and processed. When the position data of the retrograde obstacle is on the planned path of the autonomous driving vehicle, the retrograde obstacle can be classified as a first-scenario retrograde obstacle; when the position data of the retrograde obstacle is not on the planned path of the autonomous driving vehicle, the retrograde obstacle can be classified as a second-scenario retrograde obstacle.

[0088] In this embodiment, the lane area corresponding to the autonomous driving vehicle can also be determined based on the driving data and road data of the autonomous driving vehicle, and then it can be determined whether the driving data of the reverse obstacle matches the lane area corresponding to the autonomous driving vehicle. When the driving data of the reverse obstacle matches the lane area corresponding to the autonomous driving vehicle, the reverse obstacle can be classified as a first-scenario reverse obstacle; when the driving data of the reverse obstacle does not match the lane area corresponding to the autonomous driving vehicle, the reverse obstacle is classified as a second-scenario reverse obstacle.

[0089] Preferably, first, the lane area of ​​the lane where the autonomous driving vehicle is located can be determined based on the position data of the autonomous driving vehicle and the lane lines and lane widths in the planned path and road data. Secondly, the position data of the retrograde obstacle can be matched with the lane area corresponding to the autonomous driving vehicle. Thirdly, when the position data of the retrograde obstacle is located in the lane area corresponding to the autonomous driving vehicle, the retrograde obstacle can be classified as a first-scenario retrograde obstacle; when the position data of the retrograde obstacle is not located in the lane area corresponding to the autonomous driving vehicle, the retrograde obstacle can be classified as a second-scenario retrograde obstacle.

[0090] Furthermore, the first-scenario reverse obstacles determined by the above two methods can be combined to obtain all the first-scenario reverse obstacles perceived by the autonomous driving vehicle, and the second-scenario reverse obstacles determined by the above two methods can be combined to obtain all the second-scenario reverse obstacles perceived by the autonomous driving vehicle.

[0091] Here, the first-scenario reverse obstacles may include reverse obstacles on the planned path of the autonomous driving vehicle and reverse obstacles on the lane where the autonomous driving vehicle is located. The first-scenario reverse obstacles may be reverse obstacles that will definitely cause a conflict with the autonomous driving vehicle. The first-scenario reverse obstacles may be obstacles that are reverse and located on the path or lane of the vehicle.

[0092] The second scenario reverse obstacle can be a reverse obstacle that may conflict with the autonomous vehicle in the future. The second scenario reverse obstacle can be an obstacle that is reverse, located outside the path or lane of the vehicle, but has the intention of intrusion.

[0093] It can be understood that both the first scenario and the second scenario are driving environments where the ego vehicle needs to deal with retrograde obstacles. The first scenario may refer to a scenario where the road is relatively narrow, the obstacle is within the driving path of the ego vehicle, and there is a conflict between retrograde and ego vehicle driving. The retrograde obstacle in the first scenario may be a retrograde obstacle that the ego vehicle has to deal with in the first scenario. The second scenario may refer to a scenario where the obstacle is outside the path of the ego vehicle, but its predicted future trajectory has the possibility of invading the path of the ego vehicle, and there is a conflict between retrograde and ego vehicle driving. The retrograde obstacle in the second scenario may be a retrograde obstacle that the ego vehicle has to deal with in the second scenario.

[0094] Step 203: When the reverse obstacle is a first-scenario reverse obstacle, the motion vector of the autonomous driving vehicle is calculated using the first planning model based on the driving data of the autonomous driving vehicle and the driving data of the first-scenario reverse obstacle.

[0095] Step 204: Based on the driving data and motion vector of the autonomous driving vehicle, obtain a first planned trajectory of the autonomous driving vehicle.

[0096] In this embodiment, the first planning model may be a model predictive control game model.

[0097] Preferably, when the retrograde obstacle is a retrograde obstacle of the first scene, that is, the autonomous driving vehicle is in the driving environment of the first scene, first, the position data and speed of the retrograde obstacle of the first scene can be extracted from the driving data of the retrograde obstacle of the first scene, and the planned path of the autonomous driving vehicle can be extracted from the driving data of the autonomous driving vehicle. Secondly, based on the position data of the retrograde obstacle of the first scene, the motion path of the retrograde obstacle is inferred in the reverse direction of the planned path of the autonomous driving vehicle. Again, based on the position data, speed and motion path of the retrograde obstacle, the state vector of the retrograde obstacle is obtained. Again, the probability of the retrograde obstacle of the previous frame of the current planning frame to accelerate / decelerate is obtained, and based on the probability of the retrograde obstacle of the previous frame to accelerate / decelerate, the preset step size, the preset maximum acceleration or deceleration, the preset first probability estimation algorithm is used to calculate the probability of the retrograde obstacle of the current planning frame to accelerate / decelerate, and again, based on the position data, speed and planned path of the autonomous driving vehicle, the state vector of the autonomous driving vehicle is obtained. Thirdly, based on the state vector of the autonomous driving vehicle, the state vector of the first scene reverse obstacle, and the constraint conditions of the first planning model, the objective function of the first planning model can be optimized and solved to calculate the action vector of the autonomous driving vehicle. Finally, based on the driving data and action vector of the autonomous driving vehicle, the first planning trajectory of the autonomous driving vehicle to deal with the first scene reverse obstacle is obtained.

[0098] In this implementation, Figure 3 is a schematic diagram of the first scenario in the trajectory planning method of an autonomous driving vehicle provided by another embodiment of the present application. Figure 3 As shown in the figure, ego represents the autonomous driving vehicle, v1 represents the speed of the autonomous driving vehicle, i.e., the speed of the ego vehicle. obs represents the reverse obstacle, i.e., the reverse obstacle in the first scene, and v2 represents the speed of the reverse obstacle in the first scene. s represents the distance between the autonomous driving vehicle and the reverse obstacle in the first scene.

[0099] For example, for the first type of retrograde scenario, i.e., the first scenario, the model predictive control game model is used to perform trajectory planning for the ego vehicle to deal with the retrograde obstacle in the first scenario. In the process of determining the objective function of the model optimization problem of the model predictive control game model, it is assumed that the current state vector of the ego vehicle is , To obtain the current position of the vehicle, To obtain the current speed of the vehicle, To obtain the current acceleration of the vehicle, the state transfer equation is as follows (1): (1) in, is the state vector, is the action vector, and is the coefficient matrix, which can be expressed as: Assume that the reverse obstacle is currently located in front of the vehicle Distance position, the initial state of the retrograde obstacle is defined as , is the initial position of the retrograde obstacle, is the initial velocity of the reverse obstacle, is the initial acceleration of the reverse obstacle. The state transfer equation of the reverse obstacle is consistent with the state transfer equation of the ego vehicle, as shown in the following formula (2): (2) The coefficient matrix is ​​expressed as: Furthermore, in the game process of the Stackelberg game model, the ego car (ego) is the leader (leader), the reverse obstacle car (obs) is the follower (follower), the follower action semantic types are divided into acceleration (accel) and deceleration (decel), and the ego car as the leader responds to the reverse obstacle's response to the ego car, and the action semantic types can be divided into acceleration and deceleration. It can be expressed as , It can be expressed as .

[0100] Therefore, the set of state vectors of the ego vehicle and the oncoming obstacle includes , the action vector set includes .

[0101] It is understandable that the following objective functions and constraint condition design parts involving the same acceleration and deceleration actions of the ego vehicle (leader) and the reverse obstacle vehicle (follower) are no longer expressed separately.

[0102] On the one hand, for the ego car, the objective function of the ego car can be defined as formula (3): (3) in, It can represent the state cost, It can represent the cost of an action. and is the weight parameter matrix. For your car The reference state at the moment, the reference state can be defined as: in, is the speed limit of the road where the vehicle is currently located, Plan the position of the trajectory point for the previous frame, and the expected acceleration is 0.

[0103] The constraints of the ego vehicle can include equality constraints and inequality constraints. Equality constraints include initial state constraints and state transition constraints, which can be defined as: Inequality constraints can include collision constraints and reversing constraints, which can be defined as: in, It can be a preset minimum safety distance.

[0104] The bound constraints of variables can be defined as: in, The jump of the car, that is, the acceleration.

[0105] Therefore, the above optimization problem of the vehicle can be summarized as an optimal control problem, which can be expressed as formula (4): (4) st , in, and are the follower's reaction state vector and reaction action vector to the leader's current possible actions, and is the leader’s response vector to the follower’s reaction state vector and action. is the equality constraint function, is the above inequality constraint.

[0106] On the other hand, for the vehicle with the opposite direction to the obstacle, the objective function of the vehicle with the opposite direction to the obstacle can be defined as formula (5): (5) Among them, the reverse obstacle vehicle and the self-vehicle can share the weight parameter matrix and , that is, assuming that the opposite-travel obstacle vehicle and the ego vehicle have the same evaluation method for the cost. The reference state of the opposite-travel obstacle vehicle It can be defined as: in, The speed limit of the road where the vehicle with the reverse obstacle is currently located. Plan the position for the previous frame, with an expected acceleration of 0.

[0107] The constraints of the vehicle with the reverse obstacle can also include equality constraints and inequality constraints. The equality constraints include initial state constraints and state transition constraints, which can be defined as: Inequality constraints include collision constraints and reverse constraints, which can be defined as: in, and It can be a preset minimum safety distance.

[0108] Variable bound constraints can be defined as: in, The jump of the obstacle, i.e. the acceleration, can be reversed.

[0109] Therefore, the above optimization problem of the retrograde obstacle is summarized as an optimal control problem, which can be expressed as formula (6): (6) st , in, The state vector that can be the follower's guess about the action taken by the leader, and The state vector and action vector of the follower's response to this guess can be expressed as: and are the above equality constraints and inequality constraints respectively.

[0110] Here, the ego vehicle as the leader and the reverse obstacle as the follower correspond to their own optimal control problems respectively, but there is a nested relationship between the variables of both parties, and the optimization problem has duality. Therefore, to solve a nested optimization problem, the optimal control problem of the follower can be expressed as the following Lagrangian function through the Karush-Kuhn-Tucker (KKT) condition: in, is the Lagrangian multiplier corresponding to the equality constraint, is the KKT multiplier corresponding to the inequality constraint. The KKT condition can include: , Therefore, the overall model optimization problem, that is, the objective function of the first planning model, can be defined as formula (7): (7) st in, It can be the prior probability set based on the human driver's deceleration reaction, that is, the probability of deceleration for the reverse obstacle, 1- That is, it is the prior probability set based on the human driver's acceleration response, that is, the probability of accelerating when facing a reverse obstacle. is the objective function of the leader. is the leader’s equality constraint function, is the leader’s inequality constraint, and are the equality constraints and inequality constraints of the followers respectively. Can be a strain constraint.

[0111] In this way, by adding strain constraints, the continuity of the ego vehicle's reaction process can be guaranteed, and the planning results of the ego vehicle's acceleration or deceleration decision can be better integrated.

[0112] In this embodiment, in addition, the prior probability can be set based on the intention of the human driver to take action, and the intention of the human driver to take action can be summarized and statistically obtained through historical behavior. The prior probability of each planning frame is updated, and the prior probability of the current frame is The estimation is defined as formula (8): , ; , (8) in, The upper limit of the probability can be set, preferably, , The prior probability of acceleration or deceleration of the reverse obstacle in the previous frame can be calculated. Can be the acceleration state of the reverse obstacle, The maximum possible step size can be set, preferably, . and It can be the maximum acceleration / deceleration of the retrograde obstacle. Preferably, the maximum acceleration / deceleration is , Step 205: When the reverse-traffic obstacle is a second-scenario reverse-traffic obstacle, determine whether the positional relationship between the second-scenario reverse-traffic obstacle and the autonomous driving vehicle satisfies a preset relationship condition.

[0113] In this embodiment, the preset relationship condition may be that the reverse obstacle is located in front of the autonomous driving vehicle.

[0114] Specifically, conflict judgment can be performed based on the position data of the autonomous driving vehicle and the multimodal predicted trajectory of the planned path and the reverse obstacle.

[0115] Step 206: When the position relationship between the second-scenario retrograde obstacle and the autonomous driving vehicle meets a preset relationship condition, the motion vector of the autonomous driving vehicle is calculated using the second planning model based on the driving data of the autonomous driving vehicle, the first predicted trajectory and the second predicted trajectory of the second-scenario retrograde obstacle.

[0116] In this embodiment, at least one of the first predicted trajectory and the second predicted trajectory of the second scene retrograde obstacle overlaps with the planned path of the autonomous driving vehicle.

[0117] Step 207: Based on the driving data and motion vector of the autonomous driving vehicle, obtain a second planned trajectory of the autonomous driving vehicle.

[0118] In this embodiment, the second planning model may be a strain-based model predictive control model.

[0119] In this embodiment, a strain-based model predictive control model can be constructed to plan the response trajectory of the vehicle to the reverse obstacle in the second scenario based on the driving data of the autonomous driving vehicle and the first predicted trajectory and the second predicted trajectory of the reverse obstacle in the second scenario using the strain-based model predictive control model.

[0120] Optionally, first, based on the position data, speed and planned path of the autonomous driving vehicle, the state vector of the autonomous driving vehicle is determined, and the state vector of the autonomous driving vehicle may include the position, speed and acceleration of each position point on the planned path. Secondly, the state vector of the first predicted trajectory of the second scene's retrograde obstacle can be determined, and the state vector of the first predicted trajectory may include the position, speed and acceleration of each trajectory point on the first predicted trajectory. Thirdly, the state vector of the second predicted trajectory of the second scene's retrograde obstacle is determined, and the state vector of the second predicted trajectory may include the position, speed and acceleration of each trajectory point on the second predicted trajectory. Thirdly, based on the state vector of the autonomous driving vehicle, the state vector of the first predicted trajectory, the state vector of the second predicted trajectory, and the constraints of the second planning model, the objective function of the second planning model is optimized and solved, and the action vector of the autonomous driving vehicle is calculated. Thirdly, based on the driving data and action vector of the autonomous driving vehicle, the second planned trajectory of the autonomous driving vehicle is obtained.

[0121] Furthermore, the probability of a retrograde obstacle entering the planned path of the autonomous driving vehicle can be obtained, and then based on the state vector of the autonomous driving vehicle, the state vector of the first predicted trajectory, the state vector of the second predicted trajectory, the constraints of the second planning model, and the probability of a retrograde obstacle entering the planned path of the autonomous driving vehicle, the objective function of the second planning model can be optimized and solved to calculate the action vector of the autonomous driving vehicle.

[0122] The probability of a retrograde obstacle entering the planned path of the autonomous driving vehicle may be the probability that a predicted trajectory of the retrograde obstacle conflicts with the planned path of the autonomous driving vehicle.

[0123] In this implementation, Figure 4 is a schematic diagram of a second scenario in a trajectory planning method for an autonomous driving vehicle provided by another embodiment of the present application. Figure 4 As shown in the figure, ego represents the autonomous driving vehicle, i.e., the ego vehicle. v1 represents the speed of the autonomous driving vehicle, i.e., the speed of the ego vehicle. obs represents the reverse obstacle, i.e., the reverse obstacle in the second scenario, and v2 represents the speed of the reverse obstacle in the second scenario.

[0124] For example, for the second type of retrograde scenario, i.e., the second scenario, the strain-based model predictive control model is used to plan the response trajectory of the ego vehicle. In the process of constructing the objective function of the optimal control problem of the strain-based model predictive control model, similarly, the current state of the ego vehicle is , the state transfer equation is consistent with that in formula (1). The multimodal prediction trajectory of the reverse obstacle is a set of trajectory points in the Cartesian coordinate system. Then, the lane center reference line of the ego vehicle is used as the reference to transform it into the Frenet coordinate system. Since the reverse obstacle is moving in the reverse direction in front of the ego vehicle, the reverse obstacle in the second scenario should also satisfy . is the current position of the vehicle, is the position of the reverse obstacle corresponding to the current state of the vehicle.

[0125] Furthermore, the first predicted trajectory in the multi-modal predicted trajectory of the retrograde obstacle may be: The second predicted trajectory can be: .

[0126] in, is the state vector of the retrograde obstacle, defined as: Then, the objective function of the vehicle to deal with the first predicted trajectory is and the objective function of the vehicle to deal with the second predicted trajectory It can be expressed as formula (9) and (10) (9) (10) in, and is the first predicted trajectory of the vehicle to deal with the reverse obstacle The response state vector and action vector of and is the second predicted trajectory of the vehicle to deal with the reverse obstacle The response state vector and action vector, and can be the weight coefficient, The first predicted trajectory for the ego vehicle is The reference state at the moment, For the vehicle to respond to the second predicted trajectory The reference state at the moment.

[0127] It can be understood that the updating process of the reference state and the value determination process of the weight coefficient here are consistent with the relevant processes of the first scenario. Please refer to the aforementioned relevant content and will not be repeated here.

[0128] Here, the constraints can also include equality constraints and inequality constraints. The equality constraints include initial state constraints and state transition constraints, which can be defined as: Inequality constraints include collision constraints and reversing constraints, which can be defined as: in, The minimum safe distance.

[0129] Variable bound constraints can be defined as: in, The vehicle can cope with the jump of the first predicted trajectory, that is, the acceleration. The vehicle can cope with the jump of the second predicted trajectory, that is, the acceleration.

[0130] The above optimization problem can be summarized as an optimal control problem, that is, the objective function of the second programming model, which can be defined as formula (11): (11) st , , , in, The first predicted trajectory of the retrograde obstacle can be represented The probability of entering the planned path or driving lane of the ego vehicle, and They are the first predicted trajectory of the vehicle above The equality and inequality constraints of and They are the second predicted trajectory of the vehicle above equality constraints and inequality constraints. is the strain constraint.

[0131] In addition, since the focus is on the possibility that the reverse obstacle actually invades the driving path of the vehicle, the priority of considering the actual running path of the obstacle is much higher than considering its speed.

[0132] Here, illustratively, the first predicted trajectory of the obstacle is ,estimate The probability of actually invading the driving path of the vehicle is , collect the actual running trajectory point coordinates of the reverse obstacle for 5 consecutive frames (0.5s) The first predicted trajectory of the corresponding frame and the second predicted trajectory For comparison, take the minimum distance between the trajectory point and the two predicted trajectories. If the actual running trajectory point is within the trajectory in 5 consecutive frames, The closer the trajectory, the more points there are. If the match is better, then we can calculate it by formula (12): The probability of , from the trajectory The closer the trajectory, the more points there are. If the match is better, then we can calculate it by formula (13): Probability , as shown below: (12) (13) in, The upper limit of the probability that can be set is The maximum probability step size that can be set.

[0133] Step 208: Perform safety detection processing on the autonomous driving vehicle using the safety detection strategy, so that when the result of the detection processing is that the autonomous driving vehicle is driving dangerously, the braking acceleration is calculated based on the safety distance, the driving data of the autonomous driving vehicle and the driving data of the reverse obstacle, and the driving of the autonomous driving vehicle is controlled based on the braking acceleration, or, when the result of the detection processing is that the autonomous driving vehicle is driving safely, the driving of the autonomous driving vehicle is controlled based on the first planned trajectory or the second planned trajectory of the autonomous driving vehicle.

[0134] In this implementation, first, the safety distance can be calculated based on the driving data of the autonomous driving vehicle and the driving data of the reverse obstacle using a safety detection algorithm. Secondly, the relative distance between the autonomous driving vehicle and the reverse obstacle is calculated based on the driving data of the autonomous driving vehicle and the driving data of the reverse obstacle. Thirdly, it is determined whether the relative distance is less than the safety distance. If the relative distance is less than the safety distance, it can be determined that the result of the detection process is that the autonomous driving vehicle is driving dangerously. Thirdly, in the case where the result of the detection process is that the autonomous driving vehicle is driving dangerously, the braking acceleration is calculated based on the safety distance, the driving data of the autonomous driving vehicle and the driving data of the reverse obstacle, and the driving of the autonomous driving vehicle is controlled based on the braking acceleration. If the relative distance is not less than the safety distance, it can be determined that the result of the detection process is that the autonomous driving vehicle is driving safely. Thirdly, in the case where the result of the detection process is that the autonomous driving vehicle is driving safely, the driving of the autonomous driving vehicle can be controlled based on the first planned trajectory or the second planned trajectory of the autonomous driving vehicle.

[0135] In this implementation, preferably, the safety detection algorithm can be expressed as formula (14): (14) in, Can be a safe distance, can be the speed of the current retrograde obstacle, can be the current speed of the vehicle, The maximum deceleration preset for the vehicle.

[0136] Furthermore, the relative distance between the current vehicle and the obstacle can be calculated based on the current position of the vehicle and the position of the obstacle. , if the current relative distance between the vehicle and the reverse obstacle is , the vehicle should immediately decelerate to avoid the impact. The deceleration can be calculated using formula (15), that is, the braking acceleration : (15) in, Can be a safe distance, can be the speed of the current retrograde obstacle, It can be the current speed of the vehicle.

[0137] It is understandable that after step 201, the autonomous driving vehicle may be directly subjected to a safety detection process. If it is determined that the autonomous driving vehicle is in a dangerous driving situation, the braking acceleration of the vehicle may be directly calculated, and the autonomous driving vehicle may be braked. If it is determined that the autonomous driving vehicle is in a safe driving situation, subsequent steps 202 to 207 may be continued to be executed to obtain the first planned trajectory of the autonomous driving vehicle, or the second planned trajectory.

[0138] It is understandable that the logic of the safety detection strategy is that when the ego vehicle stops and the obstacle in the opposite direction hits the ego vehicle, the ego vehicle has slowed down and braked to a stop as much as possible, and the ego vehicle is not responsible for the collision. Here, when using the safety detection algorithm to calculate the safety distance, the response of the obstacle within the reaction time can be ignored. This can avoid the conservative driving decision-making of the ego vehicle caused by overly strict safety checks, and can also avoid the problem of insufficient deceleration caused by optimistic predictions.

[0139] In this implementation, Figure 5A and 5B to Fig. 9A and 9B Schematic diagram of the trajectory and speed points planned by the trajectory planning method of the autonomous driving vehicle to cope with the first scenario of the reverse obstacle provided by another embodiment of the present application. Figure 5A and 5B to Fig. 9A and 9B In the figure, the upper figure shows the actual driving trajectory. The purple-red dot (traj_L) on the left side of the upper figure represents the actual driving trajectory of the vehicle, and the light red dotted line in front represents the planned trajectory of the vehicle's selected acceleration (acc) / deceleration (dec). Figure 5A and 5B to Fig. 9A and 9B In the planning process shown in , the planned trajectory of the ego vehicle choosing acceleration (acc) becomes lighter and lighter, and the planned trajectory of the ego vehicle choosing deceleration (dec) becomes clearer and clearer. The blue point (traj_F) on the right side of the upper figure is the actual driving trajectory of the reverse obstacle. The lower figure shows the speed planning diagram, the purple point (vxL) below the lower figure represents the actual driving speed point of the ego vehicle, and the blue point (vxF) above the lower figure represents the planned speed point of the reverse obstacle. Figure 5A and 5B to Fig. 9A and 9BThe figure shows the change process of the planned trajectory and planned speed point over time t. For example, the initial position of the reverse obstacle can be defined as 40 meters (m) in front of the vehicle, the speed is 6 meters / second (m / s), and the acceleration is 0 meters / square second (m / s 2 ); the initial position of the vehicle is defined as 0 m, the initial velocity is 5 m / s, and the acceleration is 0 m / s 2 The planning time step is 0.5s, the prediction period (horizon) is 10s, and the initial confidence deceleration probability of the obstacle is 0.5. Figure 5A In the example, the initial time t=0.0 seconds (s), the probability that the reverse obstacle takes deceleration is Pd=0.50, and the probability that the reverse obstacle takes acceleration is Pa=0.50. Figure 5B In the figure, time t=0.5 s, the probability that the reverse obstacle takes deceleration is Pd=0.50, and the probability that the reverse obstacle takes acceleration is Pa=0.50; Fig. 6A In the example, at time t=2.0s, the probability that the obstacle moving in the opposite direction will decelerate is Pd=0.40, and the probability that the obstacle moving in the opposite direction will accelerate is Pa=0.60. Figure 6B In the example, at time t=2.5s, the probability that the reverse obstacle takes deceleration is Pd=0.50, and the probability that the reverse obstacle takes acceleration is Pa=0.50; Fig. 7A In the example, at time t=4.0s, the probability that the obstacle moving in the opposite direction will decelerate is Pd=0.80, and the probability that the obstacle moving in the opposite direction will accelerate is Pa=0.20. Figure 7B In the figure, at time t=4.5 s, the probability that the reverse obstacle takes deceleration is Pd=0.90, and the probability that the reverse obstacle takes acceleration is Pa=0.10; Fig. 8A In the example, at time t=7.0s, the probability that the reverse obstacle takes deceleration is Pd=0.95, and the probability that the reverse obstacle takes acceleration is Pa=0.05. Figure 8B In the figure, time t=7.5 s, the probability that the reverse obstacle takes deceleration is Pd=0.95, and the probability that the reverse obstacle takes acceleration is Pa=0.05; Fig. 9A In the example, at time t=9.0s, the probability that the obstacle moving in the opposite direction will decelerate is Pd=0.95, and the probability that the obstacle moving in the opposite direction will accelerate is Pa=0.05. Fig. 9B In the figure, time t=9.5 s, the probability that the reverse obstacle takes deceleration is Pd=0.95, and the probability that the reverse obstacle takes acceleration is Pa=0.05.

[0140] like Figure 5A and 5B to Fig. 9A and 9BAs can be seen from the figure, when the initial distance between the ego vehicle and the oncoming obstacle vehicle is far, they choose to speed up to approach the expected speed and improve efficiency. When the distance is close, the two vehicles begin to slow down to ensure longitudinal safety.

[0141] In this implementation, Fig. 10A and 10B to Fig.14A and 14B FIG. 1 is a schematic diagram of the planning result of the trajectory planning method for an autonomous driving vehicle provided in another embodiment of the present application for dealing with a reverse obstacle in the second scenario. Fig. 10A and 10B to Fig.14A and 14B The planning results may include the planned trajectory, planned speed points, planned jerk, and planned acceleration. The upper left figure shows the actual driving trajectory point diagram. The purple-red point on the left side of the upper left figure (traj_L) represents the actual driving trajectory point of the ego vehicle. The two light red lines 1 and 2, one deep and one light, represent the response trajectory of the ego vehicle in response to different modal prediction paths of the reverse obstacle, that is, the planned trajectory. The blue point on the right (traj_F) represents the actual driving trajectory point of the obstacle vehicle. In this embodiment, the actual driving trajectory of the reverse obstacle is the predicted trajectory that conflicts with the ego vehicle. The upper right figure shows the driving speed diagram of the ego vehicle and the reverse obstacle. The blue point (vF) below represents the obstacle. The actual speed of the obstructing vehicle, the purple dot (vL) above represents the planned speed point of the ego vehicle; the lower left figure represents the action vector, that is, the acceleration jerk record, the red line 1 represents the response planning action vector of the ego vehicle to the conflict prediction trajectory of the reverse obstacle, and the green line 2 represents the response planning action vector of the ego vehicle to the conflict-free path of the reverse obstacle; the lower right figure represents the acceleration acc record, the red line 1 represents the planned acceleration of the ego vehicle to the conflict prediction trajectory of the reverse obstacle, and the green line 2 represents the planned acceleration of the ego vehicle to the conflict-free prediction trajectory of the reverse obstacle. It can be seen that the green line 2 in the lower left and lower right figures becomes shallower and shallower with the change of planning time, indicating that the probability of the conflict-free prediction trajectory of the reverse obstacle is getting smaller and smaller.

[0142] Fig. 10A and 10B to Fig.14A and 14B The planned trajectory, planned speed points, planned jerk, and planned acceleration of the ego vehicle against the second scenario's reverse obstacle are shown as a function of time t. The initial position of the reverse obstacle can be defined as 50m in front of the ego vehicle, with a speed of 5m / s and an acceleration of 0; the initial position of the ego vehicle is defined as 0m, with an initial speed of 5m / s and an acceleration of 0. The planning time step is 0.5s and the horizon is 10s. The probability that the obstacle's possibly conflicting predicted trajectory initially cuts into the ego vehicle's path is 0.7. Fig. 10AIn the example, at the initial time t=0.0s, the probability that the reverse obstacle may have a conflicting predicted trajectory is P1=0.70, and the probability that the reverse obstacle may have a non-conflicting predicted trajectory is P2=0.30. Fig. 10B In the figure, at time t = 0.5 s, the probability that the reverse obstacle may have a conflicting predicted trajectory is P1 = 0.70, and the probability that the reverse obstacle may have a non-conflicting predicted trajectory is P2 = 0.30; Fig.11A At time t=1.0s, the probability that the reverse obstacle may have a conflicting predicted trajectory is P1=0.80, and the probability that the reverse obstacle may have a non-conflicting predicted trajectory is P2=0.20. Fig. 11B In the figure, at time t = 1.5 s, the probability that the reverse obstacle may have a conflicting predicted trajectory is P1 = 0.90, and the probability that the reverse obstacle may have a non-conflicting predicted trajectory is P2 = 0.10; Fig. 12A At time t=5.0s, the probability that the reverse obstacle may have a conflicting predicted trajectory is P1=0.95, and the probability that the reverse obstacle may have a non-conflicting predicted trajectory is P2=0.05. Fig. 12B In the figure, at time t=5.5 s, the probability that the reverse obstacle may have a conflicting predicted trajectory is P1=0.95, and the probability that the reverse obstacle may have a non-conflicting predicted trajectory is P2=0.05; Fig.13A At time t=7.0s, the probability that the reverse obstacle may have a conflicting predicted trajectory is P1=0.95, and the probability that the reverse obstacle may not have a conflicting predicted trajectory is P2=0.05. Fig. 13B In the example, at time t=7.5s, the probability that the reverse obstacle may have a conflicting predicted trajectory is P1=0.95, and the probability that the reverse obstacle may have a non-conflicting predicted trajectory is P2=0.05; Fig.14A At time t=9.0s, the probability that the reverse obstacle may have a conflicting predicted trajectory is P1=0.95, and the probability that the reverse obstacle may have a non-conflicting predicted trajectory is P2=0.05. Fig. 14B At time t = 9.5 s, the probability that the reverse obstacle may have a conflicting predicted trajectory is P1 = 0.95, and the probability that the reverse obstacle may have a non-conflicting predicted trajectory is P2 = 0.05.

[0143] like Fig. 10A and 10B to Fig.14A and 14B As shown in the figure, it can be seen that the vehicle predicts the paths of the two reverse obstacles with opposite conflict results, effectively judges the actual possible motion trajectories of the reverse obstacles, and responds to the reverse intentions and behaviors of the obstacles by using a contingency processing method.

[0144] In this way, by adopting the technical solution in this embodiment, it is possible to analyze and classify the reverse obstacle scenarios when passing through narrow roads, handle the reverse obstacles on the planned path of the self-vehicle based on an interactive game method, and handle the reverse obstacles with intrusion intentions outside the planned path of the self-vehicle based on a strain processing method based on multimodal prediction of obstacles, so that robustness and exploratoryness can be taken into account when the self-vehicle makes decisions and plans the trajectory to deal with reverse obstacles.

[0145] In addition, by adopting the technical solution in this embodiment, it is possible to avoid sudden braking and evasive behavior caused by overly conservative decision-making of the vehicle, improve the continuity of the vehicle's decision-making, and prevent step-like mutations in the decision-making process.

[0146] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0147] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0148] Fig.15 A structural block diagram of a trajectory planning device for an autonomous driving vehicle provided by an embodiment of the present application is shown. Fig.15 As shown. The trajectory planning device 1500 of the autonomous driving vehicle of this embodiment may include a first acquisition unit 1501, a first classification unit 1502, a first planning unit 1503 and a second planning unit 1504. The first acquisition unit 1501 is used to acquire the driving data of the autonomous driving vehicle, the driving data of the reverse obstacle, and the road data; the first classification unit 1502 is used to classify the reverse obstacle based on the driving data of the autonomous driving vehicle, the driving data of the reverse obstacle, and the road data, using a preset scene classification strategy; the first planning unit 1503 is used to respond to the reverse obstacle being a reverse obstacle of the first scene, based on the driving data of the autonomous driving vehicle and the driving data of the reverse obstacle of the first scene, obtain the first planned trajectory of the autonomous driving vehicle using a first planning model; the second planning unit 1504 is used to respond to the reverse obstacle being a reverse obstacle of the second scene, based on the driving data of the autonomous driving vehicle and the driving data of the reverse obstacle of the second scene, obtain the second planned trajectory of the autonomous driving vehicle using a second planning model.

[0149] It should be noted that part or all of the trajectory planning device of the autonomous driving vehicle of this embodiment may be an application located in the local terminal, or may also be a functional unit such as a plug-in or software development kit (SDK) set in the application located in the local terminal, or may also be a processing engine located in a network-side server, or may also be a distributed system located on the network side, for example, a processing engine or distributed system in an autonomous driving platform on the network side, etc. This embodiment does not specifically limit this.

[0150] It is understandable that the application may be a local program (nativeApp) installed on the local terminal, or may also be a webpage program (webApp) of a browser on the local terminal, which is not limited in this embodiment.

[0151] Optionally, in a possible implementation of this embodiment, the first planning unit 1503 can be used to determine the motion vector of the autonomous driving vehicle based on the driving data of the autonomous driving vehicle and the driving data of the reverse obstacle in the first scene, using a first planning model; and obtain the first planned trajectory of the autonomous driving vehicle based on the driving data of the autonomous driving vehicle and the motion vector.

[0152] Optionally, in a possible implementation of this embodiment, the first planning unit 1503 can be used to determine the state vector of the autonomous driving vehicle based on the driving data of the autonomous driving vehicle; infer the state vector of the reverse obstacle in the first scenario based on the driving data of the autonomous driving vehicle and the driving data of the reverse obstacle in the first scenario; optimize the objective function of the first planning model based on the state vector of the autonomous driving vehicle, the state vector of the reverse obstacle in the first scenario, and the constraints of the first planning model; and determine the action vector of the autonomous driving vehicle based on the result of the optimization processing.

[0153] Optionally, in a possible implementation of this embodiment, the driving data of the second-scenario reverse obstacle includes a first predicted trajectory and a second predicted trajectory, and the second planning unit 1504 can be used to determine the motion vector of the autonomous driving vehicle based on the driving data of the autonomous driving vehicle, the first predicted trajectory and the second predicted trajectory using a second planning model; and obtain the second planned trajectory of the autonomous driving vehicle based on the driving data of the autonomous driving vehicle and the motion vector.

[0154] Optionally, in a possible implementation of this embodiment, the second planning unit 1504 may be used to determine a state vector of the autonomous driving vehicle based on the driving data of the autonomous driving vehicle; determine a state vector of the first predicted trajectory of the second scenario's retrograde obstacle based on the first predicted trajectory; determine a state vector of the second predicted trajectory based on the second predicted trajectory of the second scenario's retrograde obstacle; optimize an objective function of the second planning model based on the state vector of the autonomous driving vehicle, the state vector of the first predicted trajectory, the state vector of the second predicted trajectory, and constraints of the second planning model; and determine an action vector of the autonomous driving vehicle based on a result of the optimization process.

[0155] Optionally, in a possible implementation of this embodiment, the first classification unit 1502 is used to determine whether the driving data of the reverse traffic obstacle matches the driving data of the autonomous driving vehicle; in response to the driving data of the reverse traffic obstacle matching the driving data of the autonomous driving vehicle, determine that the reverse traffic obstacle is a first-scenario reverse traffic obstacle; in response to the driving data of the reverse traffic obstacle not matching the driving data of the autonomous driving vehicle, determine that the reverse traffic obstacle is a second-scenario reverse traffic obstacle.

[0156] Optionally, in a possible implementation of this embodiment, the first classification unit 1502 is used to determine a lane area corresponding to the autonomous driving vehicle based on the driving data and road data of the autonomous driving vehicle; determine whether the driving data of the reverse obstacle matches the lane area corresponding to the autonomous driving vehicle; in response to the driving data of the reverse obstacle matching the lane area corresponding to the autonomous driving vehicle, determine that the reverse obstacle is a first-scenario reverse obstacle; in response to the driving data of the reverse obstacle not matching the lane area corresponding to the autonomous driving vehicle, determine that the reverse obstacle is a second-scenario reverse obstacle.

[0157] Optionally, in a possible implementation of this embodiment, the first acquisition unit 1501 is used to calculate a safety distance based on the driving data of the autonomous driving vehicle and the driving data of the reverse obstacle using a safety detection algorithm; calculate a relative distance between the autonomous driving vehicle and the reverse obstacle based on the driving data of the autonomous driving vehicle and the driving data of the reverse obstacle; and calculate a braking acceleration based on the safety distance, the driving data of the autonomous driving vehicle, and the driving data of the reverse obstacle when the relative distance and the safety distance meet preset conditions, so as to control the driving of the autonomous driving vehicle based on the braking acceleration.

[0158] In this embodiment, the driving data of the autonomous driving vehicle, the driving data of the wrong-traffic obstacle, and the road data can be acquired by the first acquisition unit, and then the first classification unit can classify the wrong-traffic obstacle based on the driving data of the autonomous driving vehicle, the driving data of the wrong-traffic obstacle, and the road data, using a preset scene classification strategy. The first planning unit, in response to the wrong-traffic obstacle being a first-scene wrong-traffic obstacle, obtains a first planned trajectory of the autonomous driving vehicle based on the driving data of the autonomous driving vehicle and the driving data of the first-scene wrong-traffic obstacle, and the second planning unit, in response to the wrong-traffic obstacle being a second-scene wrong-traffic obstacle, obtains a first planned trajectory of the autonomous driving vehicle based on the first planning model. The second planning model is used to obtain the second planned trajectory of the autonomous driving vehicle based on the driving data of the autonomous driving vehicle and the driving data of the reverse obstacle in the second scenario. Since the reverse obstacles can be classified into scenarios first, the autonomous driving vehicle can then plan trajectories for the reverse obstacles in different types of scenarios respectively, and the limited rational interactive game of the reverse obstacles is taken into account, the problem of the autonomous driving vehicle passing through narrow roads can be effectively solved, while avoiding the emergency braking avoidance behavior caused by the overly conservative trajectory planning of the autonomous driving vehicle, and the robustness and exploratory nature of the trajectory planning of the autonomous driving vehicle are taken into account, the continuity of the planned trajectory of the autonomous driving vehicle is improved, thereby ensuring the reliability of the autonomous driving vehicle.

[0159] In the technical solution of this application, the user personal information involved, such as the collection, storage, use, processing, transmission, provision and disclosure of user images and attribute data, etc., complies with the provisions of relevant laws and regulations and does not violate public order and good morals.

[0160] According to an embodiment of the present application, the present application also provides an electronic device, a readable storage medium and a computer program product.

[0161] According to an embodiment of the present application, further, an unmanned vehicle including the provided electronic device is provided, and the autonomous driving vehicle may include vehicles of level L2 and above. For example, the autonomous driving vehicle may include but is not limited to an autonomous driving logistics vehicle, an autonomous driving inspection vehicle, an autonomous driving delivery vehicle, an autonomous driving large vehicle, etc.

[0162] Fig.16A schematic block diagram of an example electronic device 1600 that can be used to implement an embodiment of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0163] like Fig.16 As shown, the electronic device 1600 includes a computing unit 1601, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1602 or a computer program loaded from a storage unit 1608 into a random access memory (RAM) 1603. In the RAM 1603, various programs and data required for the operation of the electronic device 1600 can also be stored. The computing unit 1601, the ROM 1602, and the RAM 1603 are connected to each other via a bus 1604. An input / output (I / O) interface 1605 is also connected to the bus 1604.

[0164] Multiple components in the electronic device 1600 are connected to the I / O interface 1605, including: an input unit 1606, such as a keyboard, a mouse, etc.; an output unit 1607, such as various types of displays, speakers, etc.; a storage unit 1608, such as a disk, an optical disk, etc.; and a communication unit 1609, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 1609 allows the electronic device 1600 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0165] The computing unit 1601 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 1601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 1601 performs the various methods and processes described above, such as a trajectory planning method for an autonomous driving vehicle. For example, in some embodiments, the trajectory planning method for an autonomous driving vehicle may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 1608. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 1600 via the ROM 1602 and / or the communication unit 1609. When the computer program is loaded into the RAM 1603 and executed by the computing unit 1601, one or more steps of the trajectory planning method for the autonomous driving vehicle described above may be performed. Alternatively, in other embodiments, the computing unit 1601 may be configured to execute a trajectory planning method for an autonomous driving vehicle in any other appropriate manner (e.g., by means of firmware).

[0166] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0167] The program code for implementing the method of the present application can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, implements the functions / operations specified in the flow chart and / or block diagram. The program code can be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0168] In the context of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0169] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0170] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0171] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0172] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in this application can be achieved, and this document is not limited here.

[0173] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A trajectory planning method for an autonomous driving vehicle, characterized in that: The method comprises: Acquire driving data of the autonomous vehicle, driving data of the reverse obstacle, and road data; Based on the driving data of the autonomous driving vehicle, the driving data of the wrong-traffic obstacle, and the road data, the wrong-traffic obstacle is classified and processed using a preset scene classification strategy; In response to the reverse obstacle being a first-scenario reverse obstacle, obtaining a first planned trajectory of the autonomous driving vehicle using a first planning model based on the driving data of the autonomous driving vehicle and the driving data of the first-scenario reverse obstacle; In response to the reverse obstacle being a second-scenario reverse obstacle, a second planned trajectory of the autonomous driving vehicle is obtained using a second planning model based on the driving data of the autonomous driving vehicle and the driving data of the second-scenario reverse obstacle.

2. The method according to claim 1, characterized in that The method of obtaining a first planned trajectory of the autonomous driving vehicle by using a first planning model based on the driving data of the autonomous driving vehicle and the driving data of the reverse obstacle in the first scene includes: Determining a motion vector of the autonomous driving vehicle using a first planning model based on the driving data of the autonomous driving vehicle and the driving data of the reverse obstacle in the first scene; Based on the driving data of the autonomous driving vehicle and the motion vector, a first planned trajectory of the autonomous driving vehicle is obtained.

3. The method according to claim 2, characterized in that The determining of the motion vector of the autonomous driving vehicle by using a first planning model based on the driving data of the autonomous driving vehicle and the driving data of the reverse obstacle in the first scene includes: Determining a state vector of the autonomous driving vehicle based on the driving data of the autonomous driving vehicle; Inferring a state vector of the first-scenario reverse-traffic obstacle based on the driving data of the autonomous driving vehicle and the driving data of the first-scenario reverse-traffic obstacle; Optimizing the objective function of the first planning model based on the state vector of the autonomous driving vehicle, the state vector of the reverse obstacle in the first scene, and the constraint conditions of the first planning model; Based on the result of the optimization process, an action vector of the autonomous driving vehicle is determined.

4. The method according to claim 1, characterized in that The driving data of the second-scenario reverse obstacle includes a first predicted trajectory and a second predicted trajectory, and obtaining a second planned trajectory of the autonomous driving vehicle by using a second planning model based on the driving data of the autonomous driving vehicle and the driving data of the second-scenario reverse obstacle includes: Determining an action vector of the autonomous driving vehicle using a second planning model based on the driving data of the autonomous driving vehicle, the first predicted trajectory, and the second predicted trajectory; Based on the driving data of the autonomous driving vehicle and the motion vector, a second planned trajectory of the autonomous driving vehicle is obtained.

5. The method according to claim 4, characterized in that The determining the action vector of the autonomous driving vehicle by using a second planning model based on the driving data of the autonomous driving vehicle, the first predicted trajectory, and the second predicted trajectory includes: Determining a state vector of the autonomous driving vehicle based on the driving data of the autonomous driving vehicle; Determining a state vector of the first predicted trajectory based on the first predicted trajectory of the retrograde obstacle in the second scenario; Determining a state vector of the second predicted trajectory based on the second predicted trajectory of the retrograde obstacle in the second scenario; Optimizing the objective function of the second planning model based on the state vector of the autonomous driving vehicle, the state vector of the first predicted trajectory, the state vector of the second predicted trajectory, and the constraints of the second planning model; Based on the result of the optimization process, an action vector of the autonomous driving vehicle is determined.

6. The method according to claim 1, characterized in that The method of classifying the reverse traffic obstacle based on the driving data of the autonomous driving vehicle, the driving data of the reverse traffic obstacle, and the road data using a preset scene classification strategy includes: Determining whether the driving data of the reverse obstacle matches the driving data of the autonomous driving vehicle; In response to the driving data of the wrong-traffic obstacle matching the driving data of the autonomous driving vehicle, determining that the wrong-traffic obstacle is a first-scenario wrong-traffic obstacle; In response to the driving data of the wrong-travel obstacle not matching the driving data of the autonomous driving vehicle, it is determined that the wrong-travel obstacle is a second-scenario wrong-travel obstacle.

7. The method according to claim 1, characterized in that The method of classifying the reverse traffic obstacle based on the driving data of the autonomous driving vehicle, the driving data of the reverse traffic obstacle, and the road data using a preset scene classification strategy further includes: Determining a lane area corresponding to the autonomous driving vehicle based on the driving data and road data of the autonomous driving vehicle; Determining whether the driving data of the oncoming obstacle matches the lane area corresponding to the autonomous driving vehicle; In response to the driving data of the wrong-travel obstacle matching the lane area corresponding to the autonomous driving vehicle, determining that the wrong-travel obstacle is a first-scenario wrong-travel obstacle; In response to the driving data of the wrong-travel obstacle not matching the lane area corresponding to the autonomous driving vehicle, the wrong-travel obstacle is determined to be a second-scenario wrong-travel obstacle.

8. The method according to claim 1, characterized in that After obtaining the driving data of the autonomous driving vehicle, the driving data of the reverse obstacle, and the road data, the method further includes: Based on the driving data of the autonomous driving vehicle and the driving data of the reverse obstacle, a safety distance is calculated using a safety detection algorithm; Based on the driving data of the autonomous driving vehicle and the driving data of the reverse obstacle, a relative distance between the autonomous driving vehicle and the reverse obstacle is calculated; When the relative distance and the safety distance meet preset conditions, the braking acceleration is calculated based on the safety distance, the driving data of the autonomous driving vehicle and the driving data of the reverse obstacle to control the driving of the autonomous driving vehicle based on the braking acceleration.

9. A trajectory planning device for an autonomous driving vehicle, characterized in that: The device comprises: A first acquisition unit, used to acquire driving data of the autonomous driving vehicle, driving data of a reverse obstacle, and road data; A first classification unit is used to classify the reverse traffic obstacle based on the driving data of the autonomous driving vehicle, the driving data of the reverse traffic obstacle, and the road data, using a preset scene classification strategy; a first planning unit, configured to obtain a first planned trajectory of the autonomous driving vehicle by using a first planning model based on the driving data of the autonomous driving vehicle and the driving data of the first scenario reverse traffic obstacle in response to the reverse traffic obstacle being a first scenario reverse traffic obstacle; The second planning unit is used to obtain a second planned trajectory of the autonomous driving vehicle by using a second planning model based on the driving data of the autonomous driving vehicle and the driving data of the second-scenario reverse traffic obstacle in response to the reverse traffic obstacle being a second-scenario reverse traffic obstacle.

10. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 8.

11. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-8.

12. A computer program product, comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 8.

13. An autonomous driving vehicle comprising the electronic device as claimed in claim 10.

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