Speed planning method and device, electronic equipment, storage medium and program product

Abstract

The invention provides a speed planning method and device, electronic equipment, a storage medium and a program product. The method comprises the following steps: determining a vehicle conflict area based on planned trajectory information of an autonomous vehicle and predicted trajectory information of a barrier vehicle; based on the historical track information of the obstacle vehicle, determining a driving intention of the obstacle vehicle for the vehicle conflict area; and adjusting the driving speed of the autonomous vehicle based on the planned trajectory information, the predicted trajectory information, the vehicle conflict area and the driving intention. According to the invention, the speed planning problem of collision conflict between the autonomous vehicle and the obstacle vehicle in an uncertain environment can be solved.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of computer technology, and in particular to a speed planning method, a speed planning device, an electronic device, a computer-readable storage medium, and a computer program product. Background Art

[0002] When the predicted trajectory of an autonomous vehicle conflicts with the predicted trajectory of other vehicles, the speed of the autonomous vehicle needs to be planned to avoid collision with other vehicles. Speed ​​planning for autonomous vehicles in an uncertain environment is a challenging task. Summary of the invention

[0003] The embodiments of the present disclosure provide a speed planning method and device, an electronic device, a storage medium, and a program product, which can solve or partially solve the above-mentioned deficiencies in the prior art or other deficiencies in the prior art.

[0004] A speed planning method provided according to the first aspect of the present disclosure includes: determining a vehicle conflict area based on planned trajectory information of an autonomous driving vehicle and predicted trajectory information of an obstacle vehicle; determining a driving intention of the obstacle vehicle with respect to the vehicle conflict area based on historical trajectory information of the obstacle vehicle; and adjusting a driving speed of the autonomous driving vehicle based on the planned trajectory information, the predicted trajectory information, the vehicle conflict area and the driving intention.

[0005] A speed planning device provided according to the second aspect of the present disclosure includes: a conflict detection module, configured to determine a vehicle conflict area based on planned trajectory information of an autonomous driving vehicle and predicted trajectory information of an obstacle vehicle; an intention prediction module, configured to determine the obstacle vehicle's driving intention with respect to the vehicle conflict area based on historical trajectory information of the obstacle vehicle; and a speed planning module, configured to adjust the driving speed of the autonomous driving vehicle based on the planned trajectory information, the predicted trajectory information, the vehicle conflict area and the driving intention.

[0006] The electronic device provided according to the third aspect of the present disclosure may include: at least one processor; and 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 so that the at least one processor can execute the speed planning method described in the first aspect of the present disclosure.

[0007] The computer-readable storage medium provided according to the fourth aspect of the present disclosure includes a computer program, and when the computer program is executed by a processor, the speed planning method described in the first aspect of the present disclosure is implemented.

[0008] The computer program product provided according to the fifth aspect of the present disclosure stores a computer program, and when the computer program is executed by a processor, the speed planning method described in the first aspect of the present disclosure is implemented.

[0009] According to the speed planning method and device, electronic device, storage medium and program product provided in the embodiments of the present invention, the vehicle conflict area is determined through the planned trajectory information of the autonomous driving vehicle and the predicted trajectory information of the obstacle vehicle; and the driving intention of the obstacle vehicle with respect to the vehicle conflict area is determined through the historical trajectory information of the obstacle vehicle; the driving speed of the autonomous driving vehicle is adjusted according to the planned trajectory information, the predicted trajectory information, the vehicle conflict area and the driving intention; the historical trajectory information of the obstacle vehicle is used as prior information to implement contingency planning of the speed of the autonomous driving vehicle, solve the speed planning problem for the collision conflict between the autonomous driving vehicle and the obstacle vehicle in an uncertain environment, effectively avoid the collision conflict between the autonomous driving vehicle and the obstacle vehicle, and improve the driving safety of the autonomous driving vehicle.

[0010] 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 disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Other features, objects and advantages of the present disclosure will become more apparent by reading the detailed description of the non-limiting embodiments made with reference to the following drawings. The drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. Among them: Figure 1 is a flow chart of a speed planning method according to an embodiment of the present disclosure; Figure 2 is a flow chart of adjusting the driving speed of an autonomous vehicle according to some embodiments of the present disclosure; Figure 3 is a flow chart for determining an objective function of a two-level optimization game model according to some embodiments of the present disclosure; Figure 4 is a schematic diagram of a vehicle conflict area between an autonomous driving vehicle and an obstacle vehicle according to some embodiments of the present disclosure; Figure 5 is a flow chart for determining constraints for a two-level optimization game model according to some embodiments of the present disclosure; Figure 6 is a flow chart of solving an objective function based on constraint conditions according to some embodiments of the present disclosure; Figure 7 is a flow chart of an application scenario of a speed planning method according to an embodiment of the present disclosure; Fig. 8A It is a schematic diagram of the trajectory of the collision between the autonomous driving vehicle and the obstacle vehicle in the oncoming vehicle scene; FIG. 8B to FIG. 8U Yes Fig. 8A A schematic diagram of speed and trajectory obtained by performing speed planning according to the speed planning method of an embodiment of the present disclosure; Fig. 9 is a block diagram of a speed planning device according to an embodiment of the present disclosure; Fig.10 is a block diagram of an example electronic device that can be used to implement embodiments of the present disclosure. DETAILED DESCRIPTION

[0012] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure 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 may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0013] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in the present disclosure may be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0014] An exemplary system architecture for implementing the speed planning method provided by the present disclosure may include a terminal device, a network, and a server. The network is used to provide a communication link between the terminal device and the server, and may include various connection types, such as a wired communication link, a wireless communication link, or an optical fiber cable.

[0015] The user can use the terminal device to interact with the server through the network to receive or send information, etc. Various client applications can be installed on the terminal device, for example, map, navigation, entertainment and other client applications.

[0016] The terminal device may be, for example, a vehicle computer system of a self-driving car, a delivery robot, or the like. The system may be implemented by hardware, by software, or by a combination of hardware and software.

[0017] The server can be hardware or software. When the server is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or it can be implemented as a single server. When the server is software, it can be implemented as multiple software or software modules (for example, used to provide distributed services), or it can be implemented as a single software or software module. No specific limitation is made here.

[0018] It should be pointed out that the execution subject of the speed planning method provided by the present disclosure (hereinafter referred to as the "execution subject") can be the server in the above system architecture, the terminal device in the above system architecture, or the server and terminal device in the above system architecture.

[0019] When the speed planning method is executed by the server, the server can determine the vehicle conflict area based on the planned trajectory information of the autonomous driving vehicle and the predicted trajectory information of the obstacle vehicle, and determine the driving intention of the obstacle vehicle with respect to the vehicle conflict area based on the historical trajectory information of the obstacle vehicle. Then, the server can adjust the driving speed of the autonomous driving vehicle based on the planned trajectory information, the predicted trajectory information, the vehicle conflict area and the driving intention, and send the control command to the terminal device, such as the vehicle-computer system of the autonomous driving vehicle, and control the speed of the autonomous driving vehicle according to the control command through the vehicle-computer system.

[0020] Another applicable scenario is to complete speed planning directly by terminal devices, such as the vehicle-mounted system of an autonomous vehicle, instead of through a server. In this case, the vehicle-mounted system can determine the vehicle conflict area based on the planned trajectory information of the autonomous vehicle and the predicted trajectory information of the obstacle vehicle, and determine the driving intention of the obstacle vehicle with respect to the vehicle conflict area based on the historical trajectory information of the obstacle vehicle. Then, the vehicle-mounted system can adjust the driving speed of the autonomous vehicle based on the planned trajectory information, predicted trajectory information, vehicle conflict area and driving intention, and control the speed of the autonomous vehicle.

[0021] In addition, speed planning can also be completed jointly by a server and a terminal device, such as the vehicle-mounted system of an autonomous driving vehicle. The present disclosure does not limit the operations performed by the server and the terminal device at this time. For example, the server can determine the vehicle conflict area based on the planned trajectory information of the autonomous driving vehicle and the predicted trajectory information of the obstacle vehicle, and determine the driving intention of the obstacle vehicle with respect to the vehicle conflict area based on the historical trajectory information of the obstacle vehicle, and then send the vehicle conflict area and driving intention to the vehicle-mounted system. The vehicle-mounted system can adjust the driving speed of the autonomous driving vehicle based on the planned trajectory information, predicted trajectory information, vehicle conflict area and driving intention, and control the speed of the autonomous driving vehicle.

[0022] In addition, in the technical solutions involved in the present disclosure, the acquisition, storage, use, processing, transportation, provision and disclosure of vehicle speed, trajectory information, etc. are in compliance with the relevant laws and regulations and do not violate public order and good morals.

[0023] Figure 1 FIG. 1 is a flow chart of a speed planning method 100 according to an embodiment of the present disclosure. Figure 1 As shown, the speed planning method 100 may include the following steps: S101. Determine a vehicle conflict area based on the planned trajectory information of the autonomous driving vehicle and the predicted trajectory information of the obstacle vehicle.

[0024] In an embodiment of the present disclosure, the execution entity may determine the vehicle conflict area based on the planned trajectory information of the autonomous driving vehicle and the predicted trajectory information of the obstacle vehicle.

[0025] In an embodiment of the present disclosure, the execution subject may first obtain the trajectory information of the autonomous driving vehicle, where the trajectory information may refer to the planned trajectory information obtained by performing trajectory planning on the autonomous driving vehicle. The planned trajectory information may include information such as the position and speed of the vehicle. For example, the execution subject may obtain the planned trajectory information of the autonomous driving vehicle from the planning module of the autonomous driving vehicle. The execution subject may also obtain the trajectory information of other vehicles, where other vehicles may refer to vehicles traveling on the road section through which the planned trajectory information of the autonomous driving vehicle passes, where the trajectory information may refer to the predicted trajectory information obtained by performing trajectory prediction on other vehicles. The predicted trajectory information may include information such as the position and speed of the vehicle. For example, the execution subject may obtain the predicted trajectory information of other vehicles from the prediction module of the autonomous driving vehicle.

[0026] Then, the execution subject can judge whether the planned trajectory information and the predicted trajectory information overlap at a certain moment based on the planned trajectory information and the predicted trajectory information. If the planned trajectory information and the predicted trajectory information overlap at a certain moment, it indicates that the autonomous driving vehicle will collide with other vehicles at that moment. The other vehicles that collide with the autonomous driving vehicle can be regarded as obstacle vehicles, and the area where the collision occurs can be regarded as the vehicle conflict area. It should be noted that the planned trajectory information of the autonomous driving vehicle can overlap with the predicted trajectory information of multiple other vehicles, so that multiple obstacle vehicles and multiple vehicle conflict areas can be obtained. The embodiments of the present disclosure do not limit the number of obstacle vehicles and the number of vehicle conflict areas.

[0027] S102: Determine the driving intention of the obstacle vehicle with respect to the vehicle conflict area based on the historical trajectory information of the obstacle vehicle.

[0028] In an embodiment of the present disclosure, the execution entity may determine the driving intention of the obstructing vehicle with respect to the vehicle conflict area according to the historical trajectory information of the obstructing vehicle.

[0029] In an embodiment of the present disclosure, the execution subject may also obtain the historical driving trajectory information of the obstructing vehicle, where the historical driving trajectory information may refer to the historical trajectory information that the obstructing vehicle has already traveled. The historical trajectory information may include information such as the location of the vehicle. For example, the execution subject may obtain the historical trajectory information of the obstructing vehicle from the electronic map server through the map application of the autonomous driving vehicle. Then, the execution subject may judge the driving intention of the obstructing vehicle with respect to the vehicle conflict area based on the historical trajectory information of the obstructing vehicle. For example, the driving intention of the obstructing vehicle with respect to the vehicle conflict area may include rushing in or giving way, where rushing in may mean that the obstructing vehicle passes through the vehicle conflict area before the autonomous driving vehicle, and giving way may mean that the obstructing vehicle allows the autonomous driving vehicle to pass through the vehicle conflict area first.

[0030] S103: Adjust the driving speed of the autonomous driving vehicle based on the planned trajectory information, the predicted trajectory information, the vehicle conflict area and the driving intention.

[0031] In an embodiment of the present disclosure, the execution entity may adjust the driving speed of the autonomous driving vehicle based on the planned trajectory information, predicted trajectory information, vehicle conflict area, and driving intention.

[0032] In the embodiments of the present disclosure, the execution subject may adjust the driving speed of the autonomous driving vehicle according to the planned trajectory information of the autonomous driving vehicle, the predicted trajectory information of the obstacle vehicle, the vehicle conflict area between the autonomous driving vehicle and the obstacle vehicle, and the driving intention of the obstacle vehicle with respect to the vehicle conflict area, so that the driving speed of the autonomous driving vehicle is adapted to the driving intention of the obstacle vehicle with respect to the vehicle conflict area, thereby controlling the autonomous driving vehicle and the obstacle vehicle not to appear in the vehicle conflict area at the same time, and avoiding collision between the autonomous driving vehicle and the obstacle vehicle. For example, when the driving intention of the obstacle vehicle with respect to the vehicle conflict area is to rush in, the execution subject may adjust the driving speed of the autonomous driving vehicle to give way and let the obstacle vehicle pass through the vehicle conflict area first; when the driving intention of the obstacle vehicle with respect to the vehicle conflict area is to rush in, the execution subject may adjust the driving speed of the autonomous driving vehicle to rush in and pass through the vehicle conflict area before the obstacle vehicle.

[0033] The embodiments of the present disclosure do not limit the implementation method of adjusting the driving speed of the autonomous driving vehicle according to the planned trajectory information, predicted trajectory information, vehicle conflict area and driving intention. In an optional example, the execution subject may adjust the driving speed of the autonomous driving vehicle through a large language model according to the planned trajectory information, predicted trajectory information, vehicle conflict area and driving intention. In another optional example, the execution subject may adjust the driving speed of the autonomous driving vehicle through a game model according to the planned trajectory information, predicted trajectory information, vehicle conflict area and driving intention. In yet another optional example, the execution subject may adjust the driving speed of the autonomous driving vehicle through a decision tree and speed planning method according to the planned trajectory information, predicted trajectory information, vehicle conflict area and driving intention.

[0034] The speed planning method 100 of the embodiment of the present disclosure determines the vehicle conflict area through the planned trajectory information of the autonomous driving vehicle and the predicted trajectory information of the obstacle vehicle; and determines the driving intention of the obstacle vehicle with respect to the vehicle conflict area through the historical trajectory information of the obstacle vehicle; adjusts the driving speed of the autonomous driving vehicle according to the planned trajectory information, the predicted trajectory information, the vehicle conflict area and the driving intention; and uses the historical trajectory information of the obstacle vehicle as prior information to implement contingency planning of the speed of the autonomous driving vehicle, solve the speed planning problem for the collision conflict between the autonomous driving vehicle and the obstacle vehicle in an uncertain environment, effectively avoid the collision conflict between the autonomous driving vehicle and the obstacle vehicle, and improve the driving safety of the autonomous driving vehicle.

[0035] It should be understood that the steps shown in method 100 are not exclusive, and other steps may be performed before, after, or between any of the steps shown. In addition, some of the steps shown may be performed simultaneously, or may be performed in different steps. Figure 1 Executed in the order shown.

[0036] Figure 2 FIG. 1 is a flowchart of adjusting the driving speed of an autonomous vehicle according to some embodiments of the present disclosure. Figure 2 As shown, step S103 adjusts the driving speed of the autonomous driving vehicle based on the planned trajectory information, the predicted trajectory information, the vehicle conflict area and the driving intention, and may include the following steps: S201. Based on the planned trajectory information, predicted trajectory information, vehicle conflict area and driving intention, a two-level optimization game model between the autonomous driving vehicle and the obstacle vehicle is established.

[0037] In an embodiment of the present disclosure, the execution entity can establish a two-level optimization game model between the autonomous driving vehicle and the obstacle vehicle based on the planned trajectory information, the predicted trajectory information, the vehicle conflict area and the driving intention.

[0038] In an embodiment of the present disclosure, a game model can be used to characterize the interaction between an autonomous driving vehicle and an obstacle vehicle. Among them, the autonomous driving vehicle can be used as a leader, and the obstacle vehicle can be used as a follower. The two-level optimization game model can include two processes: in the first process, the follower predicts the future movement of the leader and optimizes its own movement; in the second process, the leader optimizes its own movement according to the movement of the follower. The two processes of the two-level optimization game model can constitute a nested optimization problem. In some optional embodiments of the present disclosure, step S201 establishes a two-level optimization game model of an autonomous driving vehicle and an obstacle vehicle based on the planned trajectory information, the predicted trajectory information, the vehicle conflict area and the driving intention, which may include: determining the objective function of the two-level optimization game model based on the planned trajectory information, the predicted trajectory information and the driving intention; determining the constraints of the two-level optimization game model based on the planned trajectory information, the predicted trajectory information and the vehicle conflict area.

[0039] In an optional example, the autonomous driving vehicle and the obstacle vehicle may be placed in a t The states at each moment are represented as and , the trajectories of the autonomous driving vehicle and the obstacle vehicle can be represented as and ,The effective execution of the two processes of the two-level optimization game model relies on the following assumptions: Strong assumption: In a short period of time, the obstacle vehicle can well predict the trajectory of the autonomous vehicle, making .in, is the trajectory of the autonomous vehicle predicted by the obstacle vehicle, It is the actual driving trajectory of the autonomous vehicle.

[0040] Based on the above assumptions, the first process of the model can be expressed as: (Formula 1) The second process of the model can be expressed as: (Formula 2) in, and are the objective functions of the autonomous driving vehicle and the obstacle vehicle, and are the driving trajectories of the autonomous vehicle and the obstacle vehicle, and They are the optimized driving trajectories of the autonomous driving vehicle and the obstacle vehicle respectively.

[0041] For a follower, given the leader's state sequence , the optimal control problem of the follower can be expressed as follows: (Formula 3) st , .

[0042] Known follower reaction status and reaction actions , the leader’s optimal control problem can be stated as follows: (Formula 4) st , .

[0043] in, and are the state sequences corresponding to the driving trajectories of the autonomous driving vehicle and the obstacle vehicle, and are the control quantities corresponding to the driving trajectories of the autonomous driving vehicle and the obstacle vehicle, and are the inequality constraints for the optimal control problems of the autonomous driving vehicle and the obstacle vehicle, and They are the equality constraints for the optimal control problems of the autonomous driving vehicle and the obstacle vehicle, respectively.

[0044] S202. Obtain an optimization solution by solving a two-level optimization game model, and adjust the driving speed of the autonomous driving vehicle based on the optimization solution.

[0045] In an embodiment of the present disclosure, the execution entity can obtain an optimization solution by solving a two-level optimization game model, and adjust the driving speed of the autonomous driving vehicle according to the optimization solution.

[0046] In the embodiments of the present disclosure, there is no limitation on the implementation method for solving the two-level optimization game model to obtain the optimization solution. In an optional example, the two-level optimization game model can be solved by the diagonal method to obtain the optimization solution. In another optional example, the two-level optimization game model can be solved by the Karush-Kuhn-Tucker (KKT) condition to obtain the optimization solution. In another optional example, the two-level optimization game model can be solved by an artificial intelligence method to obtain the optimization solution. In some optional embodiments of the present disclosure, after constructing the two-level optimization game model by determining the objective function and the constraints, step S202 obtains the optimization solution by solving the two-level optimization game model, which may include: solving the objective function based on the constraints to obtain the optimization solution.

[0047] In some optional embodiments of the present disclosure, when the obstacle vehicle intends to rush the vehicle in the vehicle conflict area, the obstacle vehicle will accelerate and overtake the autonomous driving vehicle to pass through the vehicle conflict area first; when the obstacle vehicle intends to give way to the autonomous driving vehicle in the vehicle conflict area, the obstacle vehicle will decelerate and avoid the autonomous driving vehicle, so that the autonomous driving vehicle passes through the vehicle conflict area first. Therefore, the acceleration or deceleration probability of the obstacle vehicle can be used to represent the obstacle vehicle's driving intention in the vehicle conflict area.

[0048] Optionally, the execution subject can evaluate the acceleration or deceleration probability of the obstructing vehicle according to the historical data trend through the historical trajectory information of the obstructing vehicle. Step S102 determines the driving intention of the obstructing vehicle to the vehicle conflict area based on the historical trajectory information of the obstructing vehicle, which may include: establishing an acceleration prediction model of the obstructing vehicle based on the historical trajectory information; determining the acceleration of the current trajectory point of the obstructing vehicle through the acceleration prediction model; determining the acceleration or deceleration probability of the current trajectory point of the obstructing vehicle based on the acceleration of the current trajectory point of the obstructing vehicle and the acceleration or deceleration probability of the previous trajectory point of the obstructing vehicle, wherein the determined acceleration probability indicates the driving intention of the obstructing vehicle to rush to the vehicle conflict area, and the determined deceleration probability indicates the driving intention of the obstructing vehicle to give way to the vehicle conflict area.

[0049] In an optional example, the most recent 6 frames of driving trajectory information can be taken as the historical trajectory information of the obstacle vehicle. For example, the coordinate set of the trajectory points of the 6 frames of driving trajectory information is: , the speed of the obstacle vehicle at each frame of the trajectory information can be calculated by the difference of the position, and the calculation formula is as follows: (Formula 5) The acceleration of the vehicle at each frame of the trajectory information can be calculated by the velocity difference, and the formula is as follows: (Formula 6) The acceleration prediction model is fitted by a cubic polynomial, and its formula is as follows: (Formula 7) in, To fit the parameters, the historical acceleration information obtained by differential calculation is substituted to obtain the fitting parameters.

[0050] If the acceleration probability of the obstacle vehicle at the trajectory point of the previous frame of driving trajectory information is , the maximum intention fluctuation between frames is no greater than , then the prior probability of the trajectory point of the current frame driving trajectory information is p The calculation formula is as follows: (Formula 8) Among them, sign() is the sign function, is a fixed parameter, It is the acceleration of the trajectory point of the current frame driving trajectory information.

[0051] Figure 3 FIG. 4 is a flowchart of determining the objective function of a two-level optimization game model according to some embodiments of the present disclosure. Figure 3 As shown, based on the planned trajectory information, the predicted trajectory information and the driving intention, determining the objective function of the two-level optimization game model may include the following steps: S301. Obtain the objective function of the autonomous driving vehicle based on the planned trajectory information and the acceleration or deceleration probability of the current trajectory point of the obstacle vehicle.

[0052] In an embodiment of the present disclosure, the execution subject can obtain the objective function of the autonomous driving vehicle based on the planned trajectory information and the acceleration or deceleration probability of the current trajectory point of the obstacle vehicle. In some optional embodiments, the planned trajectory information of the autonomous driving vehicle can be a trajectory point sequence, where each trajectory point can include posture information, speed information, acceleration information, etc. The cost function of the autonomous driving vehicle can be constructed based on the state change of the trajectory point in the trajectory point sequence of the planned trajectory information, and the objective function of the autonomous driving vehicle can be obtained based on the cost function of the autonomous driving vehicle.

[0053] Optionally, step S301 obtains the objective function of the autonomous driving vehicle based on the planned trajectory information and the acceleration or deceleration probability of the current trajectory point of the obstacle vehicle, and may include: determining the cost function of the autonomous driving vehicle based on the planned trajectory information; obtaining the objective function of the autonomous driving vehicle based on the cost function of the autonomous driving vehicle and the acceleration or deceleration probability of the current trajectory point of the obstacle vehicle.

[0054] In some optional embodiments, determining the cost function of the autonomous driving vehicle based on the planned trajectory information may include: determining a state sequence and an acceleration sequence of the trajectory points of the autonomous driving vehicle based on the planned trajectory information, wherein the state of each trajectory point in the state sequence includes posture information, velocity information, and acceleration information; determining a first state change cost of the state sequence of the trajectory points of the autonomous driving vehicle relative to a target state of the autonomous driving vehicle, and a first smooth transition cost of the acceleration sequence of the trajectory points of the autonomous driving vehicle, wherein the cost function of the autonomous driving vehicle includes a first state change cost and a first smooth transition cost.

[0055] It should be noted that the target state of the autonomous driving vehicle can be set as needed, and the embodiments of the present disclosure do not limit this. For example, the speed limit of the road section through which the planned trajectory information passes can be used as the speed information of the target state, and the posture information of the target state can be determined based on the speed limit and time of the road section through which the planned trajectory information passes, and the maximum acceleration of the autonomous driving vehicle can be used as the acceleration information of the target state.

[0056] In some optional implementations, the objective function of the autonomous driving vehicle can be constructed based on the prior probability of the current trajectory point of the obstacle vehicle and the cost function of the autonomous driving vehicle. The objective function of the autonomous driving vehicle may include: an acceleration cost function and a deceleration cost function. The prior probability of the current trajectory point of the obstacle vehicle may be an acceleration probability or a deceleration probability.

[0057] Optionally, obtaining the objective function of the autonomous driving vehicle based on the cost function of the autonomous driving vehicle and the acceleration or deceleration probability of the current trajectory point of the obstacle vehicle may include: obtaining the acceleration cost function and the deceleration cost function of the autonomous driving vehicle based on the cost function of the autonomous driving vehicle; determining the weights of the acceleration cost function and the deceleration cost function based on the acceleration or deceleration probability of the current trajectory point of the obstacle vehicle; and taking the weighted sum of the acceleration cost function and the deceleration cost function to obtain the objective function of the autonomous driving vehicle.

[0058] S302: Determine the objective function of the obstacle vehicle based on the predicted trajectory information.

[0059] In an embodiment of the present disclosure, the execution subject may determine the objective function of the obstructing vehicle based on the predicted trajectory information. In some optional embodiments, the predicted trajectory information of the obstructing vehicle may be a trajectory point sequence, wherein each trajectory point may include position information, velocity information, acceleration information, etc. A cost function of the obstructing vehicle may be constructed based on the state change of the trajectory point in the trajectory point sequence of the predicted trajectory information, and the objective function of the obstructing vehicle may be obtained based on the cost function of the obstructing vehicle.

[0060] Optionally, step S302, based on the predicted trajectory information, determining the objective function of the obstacle vehicle may include: determining the cost function of the obstacle vehicle based on the predicted trajectory information as the objective function of the obstacle vehicle. In some optional implementations, determining the cost function of the obstacle vehicle based on the predicted trajectory information may include: determining the state sequence and jerk sequence of the trajectory points of the obstacle vehicle based on the predicted trajectory information, wherein the state of each trajectory point in the state sequence includes posture information, velocity information, and acceleration information; determining a second state change cost of the state sequence of the trajectory points of the obstacle vehicle relative to the target state of the obstacle vehicle, and a second smooth transition cost of the jerk sequence of the trajectory points of the obstacle vehicle, wherein the cost function of the obstacle vehicle includes a second state change cost and a second smooth transition cost.

[0061] It should be noted that the target state of the obstacle vehicle can be set as needed, and the embodiments of the present disclosure do not limit this. For example, the speed limit of the road section through which the predicted trajectory information passes can be used as the speed information of the target state, the position information of the target state can be determined based on the speed limit and time of the road section through which the predicted trajectory information passes, and the maximum acceleration of the obstacle vehicle can be used as the acceleration information of the target state.

[0062] S303. Based on the objective function of the autonomous driving vehicle and the objective function of the obstacle vehicle, the objective function of the two-level optimization game model is obtained.

[0063] In the embodiments of the present disclosure, the execution subject may obtain the objective function of the two-level optimization game model according to the objective function of the autonomous driving vehicle and the objective function of the obstacle vehicle. In some optional embodiments, the objective function of the autonomous driving vehicle and the objective function of the obstacle vehicle may be weighted and summed to obtain the objective function of the two-level optimization game model. The weights of the objective function of the autonomous driving vehicle and the objective function of the obstacle vehicle may be set as needed, and the embodiments of the present disclosure are not limited thereto.

[0064] In an optional example, the state sequence of the trajectory point is , the acceleration sequence of the trajectory points is ,in It's posture. It's speed. is the acceleration, is the jerk.

[0065] The objective function includes the state change cost and the smooth transition cost. The target state of the autonomous driving vehicle is , the target state of the obstacle vehicle is , then the cost function of the autonomous driving vehicle is as follows: (Formula 9) In Publication 9, is the state sequence of the autonomous vehicle The state at time k, is the jerk at time k in the jerk sequence of the autonomous vehicle.

[0066] The cost function of the obstacle vehicle is as follows: (Formula 10) In formula 10, is the state sequence of the obstacle vehicle The state at time k, is the jerk of the obstacle vehicle at time k in the jerk sequence.

[0067] In Formula 9 and Formula 10, Q and R are the weight parameter matrices of the obstacle vehicle and the autonomous driving vehicle. The weight parameter matrices of the obstacle vehicle and the autonomous driving vehicle can be the same or different. For the sake of convenience, in this embodiment, the obstacle vehicle and the autonomous driving vehicle use the same weight parameter matrix. Q and R can be defined as: , .

[0068] The objective function of an autonomous vehicle can be expressed as: (Formula 11) in, p is the prior probability that the obstacle vehicle will slow down in response to the autonomous vehicle, is the acceleration cost function, is the deceleration cost function.

[0069] The weight of the autonomous vehicle is , the weight of the obstacle vehicle is , then the objective function of the two-level optimization game model is as follows: (Formula 12) in, , The closer the value is to 1, the more emphasis is placed on the driving goal and feeling of the autonomous vehicle. On the contrary, it means that the more emphasis is placed on the feeling of the obstacle vehicle.

[0070] This embodiment uses the acceleration or deceleration probability of the obstacle vehicle as prior information, and can implement contingency planning for the speed of the autonomous driving vehicle. It can take into account both the situations of accelerating to overtake and decelerating to give way. It can not only meet the requirements of overtaking efficiency, but also have the robustness of giving way and deceleration to accidental events, and can meet the requirements of situations such as sudden changes in environmental conditions.

[0071] In some optional embodiments of the present disclosure, after the execution subject determines the vehicle conflict area according to the planned trajectory information of the autonomous driving vehicle and the predicted trajectory information of the obstacle vehicle, it can further determine the boundary of the vehicle conflict area. Optionally, the projection position of the overlapping area of ​​the planned trajectory of the autonomous driving vehicle and the predicted trajectory of the obstacle vehicle on their respective trajectories can be used as the boundary of the vehicle conflict area. Step S101 determines the vehicle conflict area based on the planned trajectory information of the autonomous driving vehicle and the predicted trajectory information of the obstacle vehicle, which may include: determining the overlapping area of ​​the planned trajectory and the predicted trajectory at the same time as the vehicle conflict area based on the planned trajectory information and the predicted trajectory information; determining the boundary of the vehicle conflict area based on the projection of the overlapping area on the planned trajectory and the predicted trajectory.

[0072] In an alternative example, Figure 4 As shown in the figure, CDEF is the vehicle conflict area where the trajectories of the autonomous driving vehicle and the obstacle vehicle overlap, where C is the front boundary of the autonomous driving vehicle (self-vehicle) reaching the vehicle conflict area, D is the rear boundary of the autonomous driving vehicle reaching the vehicle conflict area, E is the front boundary of the obstacle vehicle (other vehicle) reaching the vehicle conflict area, and F is the rear boundary of the obstacle vehicle reaching the vehicle conflict area.

[0073] Figure 5 FIG. 4 is a flowchart of determining the constraint conditions of a two-level optimization game model according to some embodiments of the present disclosure. Figure 5 As shown, based on the planned trajectory information, the predicted trajectory information and the vehicle conflict area, determining the constraint conditions of the two-level optimization game model may include the following steps: S501. Determine the equality constraint conditions of the two-level optimization game model based on the planned trajectory information and the predicted trajectory information.

[0074] In the embodiments of the present disclosure, the execution subject may determine the equality constraints of the two-level optimization game model based on the planning trajectory information and the predicted trajectory information. Among them, the equality constraints of the two-level optimization game model may be set as needed, and the embodiments of the present disclosure are not limited to this. In some optional embodiments, the equality constraints of the two-level optimization game model may include: initial state constraints, state transition constraints, and contingency planning constraints.

[0075] Optionally, step S501 determines the equality constraint conditions of the two-level optimization game model based on the planned trajectory information and the predicted trajectory information, which may include: obtaining the initial state constraint of the autonomous driving vehicle based on the state of the first trajectory point in the planned trajectory information being the initial state of the autonomous driving vehicle; obtaining the initial state constraint of the obstacle vehicle based on the state of the first trajectory point in the predicted trajectory information being the initial state of the obstacle vehicle; obtaining the state transfer constraint of the autonomous driving vehicle based on the states and jerk of adjacent trajectory points in the planned trajectory information satisfying the transfer matrix; obtaining the state transfer constraint of the obstacle vehicle based on the states and jerk of adjacent trajectory points in the predicted trajectory information satisfying the transfer matrix; obtaining the contingency planning constraint of the autonomous driving vehicle based on the jerk of the first trajectory point in the planned trajectory information being equal to the acceleration and deceleration of the obstacle vehicle.

[0076] In an optional example, the equality constraint may include: initial state constraint, state transition constraint and contingency planning constraint. The initial state constraint can be expressed as: (Formula 13) in, is the current state of the vehicle, is the state of the trajectory point in the first frame of the state sequence.

[0077] The state transition constraint can be expressed as: (Formula 14) Among them, A and B are transfer matrices, is the state of the trajectory point in the k+1th frame of the state sequence, is the state of the trajectory point in the kth frame of the state sequence, The jerk of the trajectory point at the kth frame of the jerk sequence. A and B can be defined as: , .

[0078] The contingency planning constraints can be expressed as: (Formula 15) in, and They are the jerk of the trajectory point in the first frame of the jerk sequence of the autonomous driving vehicle in response to the acceleration and deceleration of the obstacle vehicle.

[0079] This embodiment uses contingency planning constraints and integrates the speed planning results of acceleration or deceleration of the autonomous driving vehicle. This can improve the smoothness of the state change of the autonomous driving vehicle while ensuring the safe driving of the autonomous driving vehicle, and can greatly improve the control ability of the autonomous driving vehicle.

[0080] S502: Determine inequality constraints of the two-level optimization game model based on the planned trajectory information, the predicted trajectory information, and the boundary of the vehicle conflict area.

[0081] In the embodiments of the present disclosure, the execution subject can determine the inequality constraints of the two-level optimization game model based on the planned trajectory information, the predicted trajectory information and the boundary of the vehicle conflict area. Among them, the inequality constraints of the two-level optimization game model can be set as needed, and the embodiments of the present disclosure are not limited to this. In some optional embodiments, the equality constraints of the two-level optimization game model may include: state constraints, reversing constraints, and collision constraints.

[0082] Optionally, step S502 determines the inequality constraints of the two-level optimization game model based on the planned trajectory information, the predicted trajectory information and the boundary of the vehicle conflict area, which may include: obtaining the state constraint of the autonomous driving vehicle based on the state of each trajectory point in the planned trajectory information being between the upper boundary state and the lower boundary state of the autonomous driving vehicle; obtaining the state constraint of the obstacle vehicle based on the state of each trajectory point in the predicted trajectory information being between the upper boundary state and the lower boundary state of the obstacle vehicle; obtaining the reversing constraint of the autonomous driving vehicle based on the position of the subsequent trajectory point of adjacent trajectory points in the planned trajectory information being not less than the position of the previous trajectory point; obtaining the reversing constraint of the obstacle vehicle based on the position of the subsequent trajectory point of adjacent trajectory points in the predicted trajectory information being not less than the position of the previous trajectory point; obtaining the collision constraint based on the sum of the distance between the trajectory point of the planned trajectory information and the boundary of the vehicle conflict area and the distance between the trajectory point of the predicted trajectory information and the boundary of the vehicle conflict area at the same time being not less than a preset safety distance.

[0083] It should be noted that the upper boundary state and the lower boundary state can be set as needed, and the embodiments of the present disclosure do not limit this. For example, the speed limit of the road section can be used as the upper limit of the speed, and the speed of zero can be used as the lower limit of the speed. The upper limit of the posture can be determined based on the speed limit of the road section and time, and the posture of zero can be used as the lower limit of the posture. The maximum acceleration can be used as the upper limit of the acceleration, and the maximum deceleration can be used as the lower limit of the acceleration.

[0084] Optionally, based on the sum of the distance between the trajectory point of the planned trajectory information and the boundary of the vehicle conflict area and the distance between the trajectory point of the predicted trajectory information and the boundary of the vehicle conflict area at the same time being greater than the preset safety distance, obtaining a collision constraint may include: determining a first geometric distance based on the front boundary and the rear boundary of the vehicle conflict area on the planned trajectory and the position of the current trajectory point in the planned trajectory information; determining a second geometric distance based on the front boundary and the rear boundary of the vehicle conflict area on the predicted trajectory and the position of the current trajectory point in the predicted trajectory information; obtaining a collision constraint based on the sum of the first geometric distance and the second geometric distance being not less than the preset safety distance. The collision constraint method of this embodiment can simplify the collision checking process.

[0085] In an optional example, the inequality constraints include: state constraints, reversing constraints and collision constraints. State constraints mainly include state boundary constraints, k The upper boundary state at time , the lower boundary state at time k is , then the state constraint can be expressed as: (Formula 16) (Formula 17) The reversing constraint can be expressed as: (Formula 18) The state sequence is formed into a table of 3 rows and N columns, with the posture information as the first row, the velocity information as the second row, and the acceleration information as the third row. is the position of the vehicle at time k, is the position of the vehicle at time k+1.

[0086] The collision constraint can be expressed as: (Formula 19) in, It's a safe distance. is the geometric distance of the autonomous driving vehicle at time k, is the geometric distance of the obstacle vehicle at time k.

[0087] The geometric distance of the autonomous vehicle can be expressed as: (Formula 20) The geometric distance of the obstacle vehicle can be expressed as: (Formula 21) in, is the position of the autonomous driving vehicle at time k, is the boundary point position of the front boundary C of the vehicle conflict area, is the boundary point position of the rear boundary D of the vehicle conflict area, is the position of the obstacle vehicle at time k, is the boundary point position of the front boundary E of the vehicle conflict area, It is the boundary point position of the rear boundary F of the vehicle conflict area.

[0088] Among them, if the obstacle vehicle is driven by a human, the safety distance reserved when adopting the acceleration response strategy is Can be longer, to allow for a safe distance when taking a deceleration approach It can be shorter. Since the self-driving vehicle adopts contingency speed planning, it takes into account both accelerating to overtake and slowing down to give way, so the reserved safety distance It can be compromised and expressed as: (Formula 22) Figure 6 FIG. 4 is a flowchart of solving an objective function based on constraint conditions according to some embodiments of the present disclosure. Figure 6 As shown, step S202 solves the objective function based on the constraint conditions to obtain the optimization solution, which may include the following steps: S601, converting the objective function of the obstacle vehicle into equality constraints and inequality constraints to obtain new equality constraints and new inequality constraints.

[0089] S602. Based on the new equality constraint, the new inequality constraint, the equality constraint of the two-level optimization game model, and the inequality constraint of the two-level optimization game model, the objective function of the autonomous driving vehicle is solved to obtain an optimized solution.

[0090] In an optional example, the objective function of the obstacle vehicle can be converted into equality constraints and inequality constraints through KKT conditions. The principle of conversion through KKT conditions is as follows.

[0091] The Lagrangian function of the follower optimal control problem is expressed as follows: (Formula 23) Among them, λ is the Lagrange multiplier corresponding to the equality constraint, μ is the KKT multiplier corresponding to the inequality constraint, and the KKT conditions include: , , , , .

[0092] After KKT conditional transformation, the nested optimization problem is expressed as follows: (Formula 24) st , (1) , (2) , (3) , (4) , (5) , (6) , (7) , (8) , (9) , (10) , (11) , (12) (13) , (14) (15) Among them, p is the prior probability that the obstacle vehicle will take a deceleration response, and 1-p is the prior probability that the obstacle vehicle will take an acceleration response.

[0093] Figure 7 FIG. 1 is a flow chart of an application scenario of the speed planning method according to an embodiment of the present disclosure. Figure 7 As shown, the speed planning method includes the following steps: Step 1: Obtain the planned trajectory information of the self-driving vehicle (autonomous driving vehicle), which includes information such as position and speed, and obtain the predicted trajectory information and historical trajectory information of the other vehicle (obstacle vehicle), which includes information such as position and speed; Step 2: Evaluate the probability of the other vehicle cutting in or giving way based on historical trajectory information; Step 3: Determine the vehicle conflict area based on the planned trajectory information and the predicted trajectory information, and calculate the boundary of the vehicle conflict area; Step 4: Construct a two-level optimization game model; Step 5: Adjust the vehicle's speed by solving the two-level optimization game model to obtain the speed planning result.

[0094] Fig. 8AThis is a schematic diagram of the trajectory of the collision between the autonomous driving vehicle and the obstacle vehicle in the oncoming vehicle scene. Fig. 8A As shown in the figure, the red line aa represents the predicted trajectory of the obstacle vehicle, and the blue line bb represents the planned trajectory of the autonomous driving vehicle. In the oncoming vehicle scenario, there is an overlapping area cc between the planned trajectory of the autonomous driving vehicle and the predicted trajectory of the obstacle vehicle, and the autonomous driving vehicle and the obstacle vehicle will collide in the overlapping area cc.

[0095] FIG. 8B to FIG. 8U Yes Fig. 8A A schematic diagram of the speed and trajectory obtained by performing speed planning according to the speed planning method of an embodiment of the present disclosure. FIG. 8B to FIG. 8U As shown in the figure, the purple line dd is the speed planned by the autonomous driving vehicle, the red line ee is the trajectory of the autonomous driving vehicle corresponding to the purple line dd, the blue line ff is the speed of the obstacle vehicle, and the blue line gg is the trajectory of the obstacle vehicle corresponding to the blue line ff. It can be seen from the figure that after the autonomous driving vehicle detects the intention of the obstacle vehicle to slow down, the contingency planning process gradually prefers to accelerate and rush, and abandons the deceleration avoidance process. Among them, Pa is the probability of the autonomous driving vehicle accelerating, Pd is the probability of the autonomous driving vehicle decelerating, Traj_F is the trajectory of the obstacle vehicle, Traj_L is the trajectory of the autonomous driving vehicle, dec is deceleration, acc is acceleration, vxF is the speed of the obstacle vehicle, and vxL is the speed of the autonomous driving vehicle.

[0096] The embodiment of the present disclosure further provides a speed planning device 900, Fig. 9 FIG. 9 is a block diagram of a speed planning device 900 according to an embodiment of the present disclosure. The speed planning device 900 according to an embodiment of the present disclosure can execute the above-mentioned speed planning method 100. Fig. 9 As shown, the speed planning device 900 may include: The conflict detection module 901 is configured to determine a vehicle conflict area based on the planned trajectory information of the autonomous driving vehicle and the predicted trajectory information of the obstacle vehicle; The intention prediction module 902 is configured to determine the driving intention of the obstacle vehicle with respect to the vehicle conflict area based on the historical trajectory information of the obstacle vehicle; The speed planning module 903 is configured to adjust the driving speed of the autonomous driving vehicle based on the planned trajectory information, the predicted trajectory information, the vehicle conflict area and the driving intention.

[0097] In some optional implementations, the speed planning module 903 includes: A model building submodule is configured to build a two-level optimization game model between the autonomous driving vehicle and the obstacle vehicle based on the planned trajectory information, the predicted trajectory information, the vehicle conflict area and the driving intention; The model solving submodule is configured to obtain an optimization solution by solving the two-level optimization game model, and adjust the driving speed of the autonomous driving vehicle based on the optimization solution.

[0098] In some optional implementations, the model building submodule is further configured to: Determining an objective function of the two-level optimization game model based on the planned trajectory information, the predicted trajectory information and the driving intention; Determining the constraint conditions of the two-level optimization game model based on the planned trajectory information, the predicted trajectory information and the vehicle conflict area; The model solving submodule is further configured as follows: The objective function is solved based on the constraint conditions to obtain the optimized solution.

[0099] In some optional implementations, the intention prediction module 902 is further configured to: Based on the historical trajectory information, establishing an acceleration prediction model of the obstacle vehicle; Determining the acceleration of the current trajectory point of the obstacle vehicle through the acceleration prediction model; Based on the acceleration of the current trajectory point of the obstacle vehicle and the acceleration or deceleration probability of the previous trajectory point of the obstacle vehicle, the acceleration or deceleration probability of the current trajectory point of the obstacle vehicle is determined as the driving intention of the obstacle vehicle to rush or give way in the vehicle conflict area.

[0100] In some optional implementations, the model building submodule is further configured to: Obtaining an objective function of the autonomous driving vehicle based on the planned trajectory information and the acceleration or deceleration probability of the current trajectory point of the obstacle vehicle; Determining the objective function of the obstacle vehicle based on the predicted trajectory information; Based on the objective function of the autonomous driving vehicle and the objective function of the obstacle vehicle, the objective function of the two-level optimization game model is obtained. In some optional implementations, the model building submodule is further configured as follows: Determining a cost function of the autonomous driving vehicle based on the planned trajectory information; Obtaining an objective function of the autonomous driving vehicle based on the cost function of the autonomous driving vehicle and the probability of acceleration or deceleration of the current trajectory point of the obstacle vehicle; and / or Based on the predicted trajectory information, a cost function of the obstacle vehicle is determined as an objective function of the obstacle vehicle.

[0101] In some optional implementations, the model building submodule is further configured to: Based on the planned trajectory information, determining a state sequence and an acceleration sequence of the trajectory points of the autonomous driving vehicle, wherein the state of each trajectory point in the state sequence includes posture information, velocity information, and acceleration information; Determining a first state change cost of a state sequence of trajectory points of the autonomous driving vehicle relative to a target state of the autonomous driving vehicle, and a first smooth transition cost of an acceleration sequence of trajectory points of the autonomous driving vehicle, wherein the cost function of the autonomous driving vehicle includes the first state change cost and the first smooth transition cost; and / or Based on the predicted trajectory information, determining a state sequence and an acceleration sequence of the trajectory points of the obstacle vehicle, wherein the state of each trajectory point in the state sequence includes position information, velocity information and acceleration information; Determine a second state change cost of a state sequence of the obstacle vehicle's trajectory points relative to a target state of the obstacle vehicle, and a second smooth transition cost of an acceleration sequence of the obstacle vehicle's trajectory points, wherein the cost function of the obstacle vehicle includes a second state change cost and a second smooth transition cost.

[0102] In some optional implementations, the model building submodule is further configured to: Based on the cost function of the autonomous driving vehicle, obtaining an acceleration cost function and a deceleration cost function of the autonomous driving vehicle; Determining the weights of the acceleration cost function and the deceleration cost function based on the acceleration or deceleration probability of the current trajectory point of the obstacle vehicle; The acceleration cost function and the deceleration cost function are weightedly summed to obtain the objective function of the autonomous driving vehicle.

[0103] In some optional implementations, the conflict detection module 901 is further configured to: Based on the planned trajectory information and the predicted trajectory information, determining an overlapping area of ​​the planned trajectory and the predicted trajectory at the same time as a vehicle conflict area; Based on the projections of the overlapping area on the planned trajectory and the predicted trajectory, a boundary of the vehicle conflict area is determined.

[0104] In some optional implementations, the model building submodule is further configured to: Determining equality constraints of the two-level optimization game model based on the planned trajectory information and the predicted trajectory information; Based on the planned trajectory information, the predicted trajectory information and the boundary of the vehicle conflict area, the inequality constraint conditions of the two-level optimization game model are determined.

[0105] In some optional implementations, the model building submodule is further configured to: Based on the state of the first trajectory point in the planned trajectory information being the initial state of the autonomous driving vehicle, obtaining an initial state constraint of the autonomous driving vehicle; Based on the state of the first trajectory point in the predicted trajectory information being the initial state of the obstacle vehicle, obtaining an initial state constraint of the obstacle vehicle; Based on the states and jerk of adjacent trajectory points in the planned trajectory information satisfying a transfer matrix, a state transfer constraint of the autonomous driving vehicle is obtained; Based on the states and jerk of adjacent trajectory points in the predicted trajectory information satisfying the transfer matrix, a state transfer constraint of the obstacle vehicle is obtained; Based on the fact that the jerk of the first trajectory point in the planned trajectory information is equal to the acceleration and deceleration of the obstacle vehicle, a contingency planning constraint of the autonomous driving vehicle is obtained.

[0106] In some optional implementations, the model building submodule is further configured to: Based on the state of each trajectory point in the planned trajectory information being between the upper boundary state and the lower boundary state of the autonomous driving vehicle, obtaining a state constraint of the autonomous driving vehicle; Based on the state of each track point in the predicted track information being between the upper boundary state and the lower boundary state of the obstacle vehicle, obtaining a state constraint of the obstacle vehicle; Obtaining a reversing constraint of the autonomous driving vehicle based on the position of a subsequent trajectory point of adjacent trajectory points in the planned trajectory information being not less than the position of a previous trajectory point; Based on the position of a subsequent trajectory point of adjacent trajectory points in the predicted trajectory information being not less than the position of a previous trajectory point, a reversing constraint of the obstacle vehicle is obtained; Based on the fact that the sum of the distance between the trajectory point of the planned trajectory information and the boundary of the vehicle conflict area and the distance between the trajectory point of the predicted trajectory information and the boundary of the vehicle conflict area at the same time is not less than a preset safety distance, a collision constraint is obtained.

[0107] In some optional implementations, the model building submodule is further configured to: Determining a first geometric distance based on a front boundary and a rear boundary of the vehicle conflict area on the planned trajectory and a position of a current trajectory point in the planned trajectory information; Determining a second geometric distance based on a front boundary and a rear boundary of the vehicle conflict area on the predicted trajectory and a position of a current trajectory point in the predicted trajectory information; The collision constraint is obtained based on that the sum of the first geometric distance and the second geometric distance is not less than the preset safety distance.

[0108] In some optional implementations, the model solving submodule is further configured to: Converting the objective function of the obstacle vehicle into an equality constraint and an inequality constraint to obtain a new equality constraint and a new inequality constraint; Based on the new equality constraint, the new inequality constraint, the equality constraint of the two-level optimization game model and the inequality constraint of the two-level optimization game model, the objective function of the autonomous driving vehicle is solved to obtain the optimization solution.

[0109] In addition, an embodiment of the present disclosure also provides an electronic device, which includes 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 so that the at least one processor can execute the above-mentioned speed planning method 100.

[0110] The embodiment of the present disclosure further provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the speed planning method 100 is implemented.

[0111] The disclosed embodiment also provides a computer program product, including a computer program, which implements the above-mentioned speed planning method 100 when executed by a processor.

[0112] Fig.10 A block diagram of an electronic device 1000 that can be used to implement an example of an embodiment of the present disclosure is shown. The electronic device 1000 is intended to represent various forms of digital computers. The electronic device 1000 may also represent various forms of mobile devices that can run computing programs. 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 disclosure described and / or claimed herein.

[0113] like Fig.10 As shown, the electronic device 1000 includes a computing unit 1010, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1020 or a computer program loaded from a storage unit 1080 into a random access memory (RAM) 1030. In the RAM 1030, various programs and data required for the operation of the electronic device 1000 can also be stored. The computing unit 1010, the ROM 1020, and the RAM 1030 are connected to each other via a bus 1040. An input / output (I / O) interface 1050 is also connected to the bus 1040.

[0114] A number of components in the electronic device 1000 are connected to the I / O interface 1050, including: an input unit 1060, such as a touch screen, etc.; an output unit 1070, such as various types of displays, speakers, etc.; a storage unit 1080, such as a disk, etc.; and a communication unit 1090, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 1090 allows the electronic device 1000 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0115] The computing unit 1010 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 1010 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 1010 performs the various methods and processes described above, such as the speed planning method 100. For example, in some embodiments, the speed planning method 100 may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 1080. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 1000 via the ROM 1020 and / or the communication unit 1090. When the computer program is loaded into the RAM 1030 and executed by the computing unit 1010, one or more steps of the speed planning method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 1010 may be configured to execute the speed planning method 100 in any other appropriate manner (eg, by means of firmware).

[0116] 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), load 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.

[0117] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may 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.

[0118] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, 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.

[0119] 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 solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0120] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. 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 modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A speed planning method, characterized in that: include: Determine the vehicle conflict area based on the planned trajectory information of the autonomous driving vehicle and the predicted trajectory information of the obstacle vehicle; Determining the driving intention of the obstacle vehicle with respect to the vehicle conflict area based on the historical trajectory information of the obstacle vehicle; Based on the planned trajectory information, the predicted trajectory information, the vehicle conflict area and the driving intention, the driving speed of the autonomous driving vehicle is adjusted.

2. The method according to claim 1, characterized in that The adjusting the driving speed of the autonomous driving vehicle based on the planned trajectory information, the predicted trajectory information, the vehicle conflict area and the driving intention includes: Based on the planned trajectory information, the predicted trajectory information, the vehicle conflict area and the driving intention, a two-level optimization game model between the autonomous driving vehicle and the obstacle vehicle is established; An optimization solution is obtained by solving the two-level optimization game model, and the driving speed of the autonomous driving vehicle is adjusted based on the optimization solution.

3. The method according to claim 2, characterized in that The two-level optimization game model between the autonomous driving vehicle and the obstacle vehicle is established based on the planned trajectory information, the predicted trajectory information, the vehicle conflict area and the driving intention, including: Determining an objective function of the two-level optimization game model based on the planned trajectory information, the predicted trajectory information and the driving intention; Determining the constraint conditions of the two-level optimization game model based on the planned trajectory information, the predicted trajectory information and the vehicle conflict area; The step of obtaining an optimization solution by solving the two-level optimization game model includes: The objective function is solved based on the constraint conditions to obtain the optimized solution.

4. The method according to claim 3, characterized in that The determining, based on the historical trajectory information of the obstacle vehicle, the driving intention of the obstacle vehicle with respect to the vehicle conflict area includes: Based on the historical trajectory information, establishing an acceleration prediction model of the obstacle vehicle; Determining the acceleration of the current trajectory point of the obstacle vehicle through the acceleration prediction model; Based on the acceleration of the current trajectory point of the obstacle vehicle and the acceleration or deceleration probability of the previous trajectory point of the obstacle vehicle, the acceleration or deceleration probability of the current trajectory point of the obstacle vehicle is determined, wherein the determined acceleration probability indicates the driving intention of the obstacle vehicle to rush into the vehicle conflict area, and the determined deceleration probability indicates the driving intention of the obstacle vehicle to give way to the vehicle conflict area.

5. The method according to claim 4, characterized in that The determining of the objective function of the two-level optimization game model based on the planned trajectory information, the predicted trajectory information and the driving intention includes: Obtaining an objective function of the autonomous driving vehicle based on the planned trajectory information and the acceleration or deceleration probability of the current trajectory point of the obstacle vehicle; Determining the objective function of the obstacle vehicle based on the predicted trajectory information; Based on the objective function of the autonomous driving vehicle and the objective function of the obstacle vehicle, the objective function of the two-level optimization game model is obtained.

6. The method according to claim 5, characterized in that Based on the planned trajectory information and the acceleration or deceleration probability of the current trajectory point of the obstacle vehicle, the objective function of the autonomous driving vehicle is obtained, including: Determining a cost function of the autonomous driving vehicle based on the planned trajectory information; Obtaining an objective function of the autonomous driving vehicle based on the cost function of the autonomous driving vehicle and the probability of acceleration or deceleration of the current trajectory point of the obstacle vehicle; and / or Determining the objective function of the obstacle vehicle based on the predicted trajectory information includes: Based on the predicted trajectory information, a cost function of the obstacle vehicle is determined as an objective function of the obstacle vehicle.

7. The method according to claim 6, characterized in that The determining, based on the planned trajectory information, a cost function of the autonomous driving vehicle comprises: Based on the planned trajectory information, determining a state sequence and an acceleration sequence of the trajectory points of the autonomous driving vehicle, wherein the state of each trajectory point in the state sequence includes posture information, velocity information, and acceleration information; Determining a first state change cost of a state sequence of trajectory points of the autonomous driving vehicle relative to a target state of the autonomous driving vehicle, and a first smooth transition cost of an acceleration sequence of trajectory points of the autonomous driving vehicle, wherein the cost function of the autonomous driving vehicle includes the first state change cost and the first smooth transition cost; and / or The step of determining the cost function of the obstacle vehicle based on the predicted trajectory information includes: Based on the predicted trajectory information, determining a state sequence and an acceleration sequence of the trajectory points of the obstacle vehicle, wherein the state of each trajectory point in the state sequence includes position information, velocity information and acceleration information; Determine a second state change cost of a state sequence of the obstacle vehicle's trajectory points relative to a target state of the obstacle vehicle, and a second smooth transition cost of an acceleration sequence of the obstacle vehicle's trajectory points, wherein the cost function of the obstacle vehicle includes a second state change cost and a second smooth transition cost.

8. The method according to claim 6, characterized in that The objective function of the autonomous driving vehicle is obtained based on the cost function of the autonomous driving vehicle and the acceleration or deceleration probability of the current trajectory point of the obstacle vehicle, including: Based on the cost function of the autonomous driving vehicle, obtaining an acceleration cost function and a deceleration cost function of the autonomous driving vehicle; Determining the weights of the acceleration cost function and the deceleration cost function based on the acceleration or deceleration probability of the current trajectory point of the obstacle vehicle; The acceleration cost function and the deceleration cost function are weightedly summed to obtain the objective function of the autonomous driving vehicle.

9. The method according to any one of claims 5 to 8, characterized in that: The determining of the vehicle conflict area based on the planned trajectory information of the autonomous driving vehicle and the predicted trajectory information of the obstacle vehicle includes: Based on the planned trajectory information and the predicted trajectory information, determining an overlapping area of ​​the planned trajectory and the predicted trajectory at the same time as a vehicle conflict area; Based on the projections of the overlapping area on the planned trajectory and the predicted trajectory, a boundary of the vehicle conflict area is determined.

10. The method according to claim 9, characterized in that The determining of the constraint conditions of the two-level optimization game model based on the planned trajectory information, the predicted trajectory information and the vehicle conflict area includes: Determining equality constraints of the two-level optimization game model based on the planned trajectory information and the predicted trajectory information; Based on the planned trajectory information, the predicted trajectory information and the boundary of the vehicle conflict area, the inequality constraint conditions of the two-level optimization game model are determined.

11. The method according to claim 10, characterized in that The determining of the equality constraint conditions of the two-level optimization game model based on the planned trajectory information and the predicted trajectory information includes: Based on the state of the first trajectory point in the planned trajectory information being the initial state of the autonomous driving vehicle, obtaining an initial state constraint of the autonomous driving vehicle; Based on the state of the first trajectory point in the predicted trajectory information being the initial state of the obstacle vehicle, obtaining an initial state constraint of the obstacle vehicle; Based on the states and jerk of adjacent trajectory points in the planned trajectory information satisfying a transfer matrix, a state transfer constraint of the autonomous driving vehicle is obtained; Based on the states and jerk of adjacent trajectory points in the predicted trajectory information satisfying the transfer matrix, a state transfer constraint of the obstacle vehicle is obtained; Based on the fact that the jerk of the first trajectory point in the planned trajectory information is equal to the acceleration and deceleration of the obstacle vehicle, a contingency planning constraint of the autonomous driving vehicle is obtained.

12. The method according to claim 10, characterized in that The determining of the inequality constraint conditions of the two-level optimization game model based on the planned trajectory information, the predicted trajectory information and the boundary of the vehicle conflict area includes: Based on the state of each trajectory point in the planned trajectory information being between the upper boundary state and the lower boundary state of the autonomous driving vehicle, obtaining a state constraint of the autonomous driving vehicle; Based on the state of each track point in the predicted track information being between the upper boundary state and the lower boundary state of the obstacle vehicle, obtaining a state constraint of the obstacle vehicle; Obtaining a reversing constraint of the autonomous driving vehicle based on the position of a subsequent trajectory point of adjacent trajectory points in the planned trajectory information being not less than the position of a previous trajectory point; Based on the position of a subsequent trajectory point of adjacent trajectory points in the predicted trajectory information being not less than the position of a previous trajectory point, a reversing constraint of the obstacle vehicle is obtained; Based on the fact that the sum of the distance between the trajectory point of the planned trajectory information and the boundary of the vehicle conflict area and the distance between the trajectory point of the predicted trajectory information and the boundary of the vehicle conflict area at the same time is not less than a preset safety distance, a collision constraint is obtained.

13. The method according to claim 12, characterized in that The collision constraint is obtained based on the fact that the sum of the distance between the trajectory point of the planned trajectory information and the boundary of the vehicle conflict area and the distance between the trajectory point of the predicted trajectory information and the boundary of the vehicle conflict area at the same time is greater than the preset safety distance, including: Determining a first geometric distance based on a front boundary and a rear boundary of the vehicle conflict area on the planned trajectory and a position of a current trajectory point in the planned trajectory information; Determining a second geometric distance based on a front boundary and a rear boundary of the vehicle conflict area on the predicted trajectory and a position of a current trajectory point in the predicted trajectory information; The collision constraint is obtained based on that the sum of the first geometric distance and the second geometric distance is not less than the preset safety distance.

14. The method according to any one of claims 10 to 13, characterized in that: Solving the objective function based on the constraint condition to obtain the optimization solution includes: Converting the objective function of the obstacle vehicle into an equality constraint and an inequality constraint to obtain a new equality constraint and a new inequality constraint; Based on the new equality constraint, the new inequality constraint, the equality constraint of the two-level optimization game model and the inequality constraint of the two-level optimization game model, the objective function of the autonomous driving vehicle is solved to obtain the optimization solution.

15. A speed planning device, characterized in that: include: a conflict detection module configured to determine a vehicle conflict area based on the planned trajectory information of the autonomous driving vehicle and the predicted trajectory information of the obstacle vehicle; an intention prediction module, configured to determine the driving intention of the obstacle vehicle with respect to the vehicle conflict area based on the historical trajectory information of the obstacle vehicle; The speed planning module is configured to adjust the driving speed of the autonomous driving vehicle based on the planned trajectory information, the predicted trajectory information, the vehicle conflict area and the driving intention.

16. An electronic device, characterized in that: include: 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, the instructions being executed by the at least one processor so that the at least one processor can execute the speed planning method described in any one of claims 1 to 14.

17. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the speed planning method as described in any one of claims 1 to 14 is implemented.

18. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the speed planning method as described in any one of claims 1 to 14 is implemented.

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