Trajectory data processing method, device, computer equipment and storage medium

Abstract

A trajectory data processing method comprises: obtaining trajectory data, road information, route data and road condition data within a preset distance range of a target vehicle within a preset time period; inputting the trajectory data, road condition data, road information and route data into a data simulation unit, wherein the data simulation unit comprises current trajectory planning parameters, and generating a simulated trajectory of the target vehicle according to the trajectory data, road condition data, road information, route data and current trajectory planning parameters; performing cost calculation on the simulated trajectory to obtain a cost value corresponding to the simulated trajectory; adjusting the current trajectory planning parameters according to the cost value, using the adjusted trajectory planning parameters as the current trajectory planning parameters, and returning to the step of generating a simulated trajectory of the target vehicle according to the trajectory data, road condition data, road information, route data and current trajectory planning parameters until a preset condition is met, stopping the parameter adjustment, and obtaining the target trajectory planning parameters.

Description

Technical Field

[0001] The present application relates to a trajectory data processing method, device, computer equipment and storage medium. Background Art

[0002] The development of artificial intelligence technology has promoted the development of autonomous driving technology. In the process of autonomous driving, autonomous vehicles will face increasingly open and complex traffic scenarios. The driving behavior of autonomous vehicles in traffic scenarios plays a decisive role in the safety of autonomous vehicles. Trajectory planning, as a core component of driving behavior, will set a large number of parameters in the trajectory planning algorithm used by the trajectory planner in order to cope with complex traffic scenarios. Due to the complex balance between parameters, it becomes extremely difficult to adapt to diverse traffic scenarios by adjusting parameters. In order to make the trajectory planning algorithm adapt to diverse traffic scenarios, the traditional method is to adjust the parameters for diverse traffic scenarios by learning the trajectory planning parameters in the trajectory planning algorithm, such as the inverse reinforcement learning method, so that in the process of autonomous driving, the trajectory planning algorithm with adjusted parameters is used for trajectory planning.

[0003] However, the traditional method is only suitable for a specific trajectory planning algorithm. When the traditional method is used to adjust the parameters of a trajectory planning algorithm other than the specific planning algorithm, the parameters of the trajectory planning algorithm will be inaccurate. Summary of the invention

[0004] According to various embodiments disclosed in the present application, a trajectory data processing method, apparatus, computer device, and storage medium are provided that can improve the parameter accuracy of a trajectory planning algorithm.

[0005] A trajectory data processing method, comprising:

[0006] Acquire the trajectory data of the target vehicle within a preset time period, the road condition data within a preset distance range from the target vehicle, the road information corresponding to the target vehicle within the preset time period, and the route data of the target vehicle within the preset time period;

[0007] Inputting the trajectory data, the road condition data, the road information and the route data into a data simulation unit, wherein the data simulation unit includes current trajectory planning parameters, and generating a simulated trajectory of the target vehicle according to the trajectory data, the road condition data, the road information, the route data and the current trajectory planning parameters by the data simulation unit;

[0008] Performing cost calculation on the simulation trajectory to obtain a cost value corresponding to the simulation trajectory; and

[0009] The current trajectory planning parameters are adjusted according to the cost value to obtain adjusted trajectory planning parameters, the adjusted trajectory planning parameters are used as the current trajectory planning parameters, and the process returns to the step of generating a simulated trajectory of the target vehicle according to the trajectory data, the road condition data, the road information, the route data and the current trajectory planning parameters by the data simulation unit until a preset condition is met, and parameter adjustment is stopped to obtain the target trajectory planning parameters.

[0010] A trajectory data processing device, comprising:

[0011] A data acquisition module, used to acquire the trajectory data of the target vehicle within a preset time period, the road condition data within a preset distance range from the target vehicle, the road information corresponding to the target vehicle within the preset time period, and the route data of the target vehicle within the preset time period;

[0012] A data simulation module, used for inputting the trajectory data, the road condition data, the road information and the route data into a data simulation unit, wherein the data simulation unit includes current trajectory planning parameters; and generating a simulated trajectory of the target vehicle according to the trajectory data, the road condition data, the road information, the route data and the current trajectory planning parameters by the data simulation unit;

[0013] a cost calculation module, used to perform cost calculation on the simulation trajectory to obtain a cost value corresponding to the simulation trajectory; and

[0014] A parameter adjustment module is used to adjust the current trajectory planning parameters according to the cost value to obtain the adjusted trajectory planning parameters, use the adjusted trajectory planning parameters as the current trajectory planning parameters, and return to the step of generating the simulated trajectory of the target vehicle according to the trajectory data, the road condition data, the road information, the route data and the current trajectory planning parameters by the data simulation unit until the preset conditions are met, stop parameter adjustment, and obtain the target trajectory planning parameters.

[0015] A computer device includes a memory and one or more processors, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processors, the one or more processors perform the following steps:

[0016] Acquire the trajectory data of the target vehicle within a preset time period, the road condition data within a preset distance range from the target vehicle, the road information corresponding to the target vehicle within the preset time period, and the route data of the target vehicle within the preset time period;

[0017] Inputting the trajectory data, the road condition data, the road information and the route data into a data simulation unit, wherein the data simulation unit includes current trajectory planning parameters, and generating a simulated trajectory of the target vehicle according to the trajectory data, the road condition data, the road information, the route data and the current trajectory planning parameters by the data simulation unit;

[0018] Performing cost calculation on the simulation trajectory to obtain a cost value corresponding to the simulation trajectory; and

[0019] The current trajectory planning parameters are adjusted according to the cost value to obtain adjusted trajectory planning parameters, the adjusted trajectory planning parameters are used as the current trajectory planning parameters, and the process returns to the step of generating a simulated trajectory of the target vehicle according to the trajectory data, the road condition data, the road information, the route data and the current trajectory planning parameters by the data simulation unit until a preset condition is met, and parameter adjustment is stopped to obtain the target trajectory planning parameters.

[0020] One or more non-volatile computer-readable storage media storing computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to perform the following steps:

[0021] Acquire the trajectory data of the target vehicle within a preset time period, the road condition data within a preset distance range from the target vehicle, the road information corresponding to the target vehicle within the preset time period, and the route data of the target vehicle within the preset time period;

[0022] Inputting the trajectory data, the road condition data, the road information and the route data into a data simulation unit, wherein the data simulation unit includes current trajectory planning parameters, and generating a simulated trajectory of the target vehicle according to the trajectory data, the road condition data, the road information, the route data and the current trajectory planning parameters by the data simulation unit;

[0023] Performing cost calculation on the simulation trajectory to obtain a cost value corresponding to the simulation trajectory; and

[0024] The current trajectory planning parameters are adjusted according to the cost value to obtain adjusted trajectory planning parameters, the adjusted trajectory planning parameters are used as the current trajectory planning parameters, and the process returns to the step of generating a simulated trajectory of the target vehicle according to the trajectory data, the road condition data, the road information, the route data and the current trajectory planning parameters by the data simulation unit until a preset condition is met, and parameter adjustment is stopped to obtain the target trajectory planning parameters.

[0025] The details of one or more embodiments of the present application are set forth in the following drawings and description. Other features and advantages of the present application will become apparent from the description, drawings, and claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 A diagram of an application environment of a trajectory data processing method in one or more embodiments.

[0028] Figure 2 Schematic diagram of a flow chart of a trajectory data processing method in one or more embodiments.

[0029] Figure 3 A flowchart of the steps of adjusting the current trajectory planning parameters in one or more embodiments is shown.

[0030] Figure 4 A block diagram of a trajectory data processing apparatus in one or more embodiments.

[0031] Figure 5 is a block diagram of a computer device in accordance with one or more embodiments. DETAILED DESCRIPTION

[0032] In order to make the technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0033] The trajectory data processing method provided in this application can be applied to Figure 1In the application environment shown. The terminal 102 communicates with the server 104 through the network. When it is necessary to adjust the trajectory planning parameters, the terminal 102 can send a parameter adjustment request to the server 104. After obtaining the parameter adjustment request, the server 104 parses the parameter adjustment request to obtain the trajectory data of the target vehicle within a preset time period, the road condition data within a preset distance range from the target vehicle, the road information where the target vehicle is located, and the route data of the target vehicle within a preset time period. It can also be that the server 104 sends a parameter adjustment instruction to the terminal 102, and the terminal 102 obtains the trajectory data of the target vehicle within a preset time period, the road condition data within a preset distance range from the target vehicle, the road information where the target vehicle is located, and the route data of the target vehicle within a preset time period according to the parameter adjustment instruction, and sends the obtained data to the server 104. The server 104 inputs the trajectory data, road condition data, road information and route data into the data simulation unit, which includes the current trajectory planning parameters. The data simulation unit generates the simulated trajectory of the target vehicle according to the trajectory data, road condition data, road information, route data and the current trajectory planning parameters, so that the server 104 calculates the cost of the simulated trajectory, obtains the cost value corresponding to the simulated trajectory, and then adjusts the current trajectory planning parameters according to the cost value to obtain the adjusted trajectory planning parameters, and uses the adjusted trajectory planning parameters as the current trajectory planning parameters, and returns to the step of generating the simulated trajectory of the target vehicle according to the trajectory data, road condition data, road information, route data and the current trajectory planning parameters through the data simulation unit, until the preset conditions are met, the parameter adjustment is stopped, and the target planning parameters are obtained. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers and portable wearable devices. The server 104 can be implemented with an independent server or a server cluster consisting of multiple servers.

[0034] In one embodiment, if Figure 2 As shown, a trajectory data processing method is provided, which is applied to Figure 1 The server in the example is used to illustrate the following steps:

[0035] Step 202, obtaining the trajectory data of the target vehicle within a preset time period, the road condition data within a preset distance range from the target vehicle, the road information where the target vehicle is located, and the route data of the target vehicle within the preset time period.

[0036] Autonomous vehicles will face increasingly open and complex traffic scenarios. The driving behavior of autonomous vehicles in traffic scenarios plays a decisive role in the safety of autonomous vehicles, and trajectory planning, as a core component of driving behavior, will set a large number of parameters in the trajectory planning algorithm used by the trajectory planner in order to cope with complex traffic scenarios. In the trajectory planning process, the driving route within a period of time can be planned through location information, surrounding environment information, etc., and specific actions can be performed to change the driving state of the autonomous vehicle, such as parking, going straight, changing lanes, turning, etc. The trajectory data processing method provided in this application can be applied to any trajectory planning algorithm to determine the optimal trajectory planning parameters for any complex and diverse traffic scene. For example, the trajectory planning algorithm may include a rapidly-exploring random tree (RRT) method, a visibility map method, a probability roadmap method (PRM), etc. Traffic scenes may include lane changing scenes, turning scenes, lane keeping scenes, parking scenes, etc.

[0037] The trajectory data of the target vehicle within the preset time period refers to the trajectory data of the target vehicle during actual driving, and the road condition data within the preset distance range from the target vehicle refers to the trajectory data of the environmental vehicles within the preset distance range from the vehicle during actual driving. Both the trajectory data and the road condition data are recorded historical data. The preset time period is the time period selected for adjusting the trajectory planning parameters. The target vehicle refers to any vehicle used for autonomous driving, and its trajectory data is used to adjust the trajectory planning parameters. The road condition data within the preset distance range from the target vehicle refers to the trajectory data of the environmental vehicles around the target vehicle. Route data refers to the destination data that the target vehicle needs to reach.

[0038] In order to enable the autonomous driving vehicle to accurately plan the trajectory in the traffic scene, the parameters of the trajectory planning algorithm used in the traffic scene can be adjusted in advance. Specifically, the server can obtain the parameter adjustment request sent by the terminal, parse the parameter adjustment request, and obtain the trajectory data of the target vehicle within the preset time period, the road condition data within the preset distance range from the target vehicle, the road information where the target vehicle is located, and the route data of the target vehicle within the preset time period. The server can also send a parameter adjustment instruction to the terminal, and the terminal obtains the trajectory data of the target vehicle within the preset time period, the road condition data within the preset distance range from the target vehicle, the road information where the target vehicle is located, and the route data of the target vehicle within the preset time period according to the parameter adjustment instruction, and sends the obtained data to the server. The trajectory data of the target vehicle may include the position, speed, acceleration, jerk, etc. of the target vehicle at each moment. The road condition data within the preset distance range from the target vehicle includes the position, speed, acceleration, jerk, etc. of the environmental vehicles within the preset distance range from the target vehicle at each moment. The road information where the target vehicle is located may include the traffic lane, road boundary point, lane line information, etc. where the target vehicle is located, and the road information may be obtained from a high-precision map. The route data may include the destination identification of the target vehicle, the location of the destination, etc. For example, the preset time period may be 40 seconds. The preset distance range may be a range with the location of the target vehicle as the center and a distance radius of 10 meters.

[0039] The target vehicle's trajectory data within a preset time period, the road condition data within a preset distance range from the target vehicle, the road information where the target vehicle is located, and the route data of the target vehicle within a preset time period can be extracted by the terminal from the on-board computer device. The on-board computer device is installed in the autonomous driving vehicle, and the on-board computer device can store the target vehicle's trajectory data, the road condition data within a damaged distance range from the target vehicle, the road information where the target vehicle is located, and the route data of the target vehicle within a preset time period.

[0040] Furthermore, the server can also obtain a parameter adjustment request sent by the terminal, parse the parameter adjustment request, obtain the request parameters, and extract the trajectory data of the target vehicle within a preset time period, the road condition data within a preset distance range from the target vehicle, the road information where the target vehicle is located, and the route data of the target vehicle within a preset time period in the on-board computer device according to the request parameters.

[0041] Step 204, the trajectory data, road condition data, road information and route data are input into a data simulation unit, the data simulation unit includes current trajectory planning parameters, and the data simulation unit generates a simulated trajectory of the target vehicle according to the trajectory data, road condition data, road information, route data and current trajectory planning parameters.

[0042] The data simulation unit is used to plan the trajectory and generate the simulated trajectory according to the planned trajectory. The simulated trajectory refers to the trajectory of the target vehicle running in the data simulation unit during the data simulation process.

[0043] After acquiring the trajectory data, road condition data, road information and route data, the server calls the data simulation unit and inputs the trajectory data, road condition data, road information and route data into the data simulation unit. The data simulation unit includes the current trajectory planning parameters, so that the data simulation unit can simulate the trajectory of the target vehicle within a preset time period according to the trajectory data, road condition data, road information, route data and the current trajectory planning parameters.

[0044] Specifically, the server can process the trajectory data, road condition data, road information and route data by framing through the data simulation unit to obtain multiple frames of data. Each frame of data includes the trajectory data, road condition data, road information and route data corresponding to the frame. Thus, the data simulation unit generates a simulated trajectory segment corresponding to each frame of data according to the current trajectory planning parameters. The current trajectory planning parameters can be pre-specified parameters that need to be adjusted. A simulated trajectory segment refers to the trajectory of the target vehicle controlled to run in each frame during the data simulation process. Furthermore, the data simulation unit generates a simulated trajectory segment corresponding to each frame of data in sequence according to the time sequence between each frame of data. Then, the data simulation unit generates a simulated trajectory of the target vehicle according to the simulated trajectory segments corresponding to multiple frames of data. The simulated trajectory may include the position, speed, acceleration, jerk, etc. at each moment.

[0045] Step 206 , performing cost calculation on the simulation trajectory to obtain a cost value corresponding to the simulation trajectory.

[0046] In order to better adjust the trajectory planning parameters, the server can calculate the cost of the simulated trajectory. There are many ways to calculate the cost. The cost can be calculated by calculating the similarity between the simulated trajectory and the trajectory data of the target vehicle, or by comparing the parameter values ​​of each driving parameter of the target vehicle and the environment vehicle within a preset distance range during the trajectory simulation. There is no limitation on the cost calculation method, and the corresponding cost calculation method can be adopted according to actual needs.

[0047] In one embodiment, performing cost calculation on the simulated trajectory to obtain a cost value corresponding to the simulated trajectory includes: calculating the similarity between the trajectory data and the simulated trajectory; and obtaining the cost value corresponding to the simulated trajectory according to the similarity calculation.

[0048] When the server calculates the cost by calculating the similarity between the simulated trajectory and the trajectory data of the target vehicle, the server can use the imitation cost method to calculate the distance between the trajectory data and the simulated trajectory, and use the distance as the similarity. The smaller the distance, the more similar the trajectory data is to the simulated trajectory. Thus, the server uses the imitation cost method to calculate the cost value corresponding to the simulated trajectory according to the calculated similarity, and the cost value corresponding to the simulated trajectory can be the weighted sum of multiple cost values. Specifically, the server determines the multiple cost values ​​to be calculated according to the cost function in the imitation cost method, and then calculates the cost value corresponding to the simulated trajectory according to the calculated similarity, the cost function and the cost weight corresponding to each cost value. The cost weights corresponding to the multiple cost values ​​in the cost function can be used as trajectory planning parameters. For example, when the cost function is the weighted sum of efficiency cost, safety cost and comfort cost, if it is necessary to automatically adjust the weights corresponding to the three costs of efficiency cost, safety cost and comfort cost, the weights corresponding to the three costs can be used as trajectory planning parameters. In this embodiment, the cost calculation is performed by calculating the similarity between the simulated trajectory of the trajectory data and the trajectory data of the target vehicle, so that a driving trajectory that is more in line with human driving habits can be obtained in the subsequent automatic driving process, thereby improving the flexibility of trajectory planning.

[0049] In one embodiment, performing a cost calculation on a simulated trajectory to obtain a cost value corresponding to the simulated trajectory includes: determining driving trajectory data corresponding to preset driving parameters in the simulated trajectory; determining driving road condition data corresponding to the preset driving parameters in the road condition data; and calculating the cost value corresponding to the simulated trajectory based on the driving trajectory data and the driving road condition data.

[0050] When the server performs cost calculation by comparing the parameter values ​​of each driving parameter in the target vehicle and the environment vehicle within the preset distance range during the trajectory simulation, the driving trajectory data corresponding to the preset driving parameters can be determined in the simulated trajectory, and the driving road condition data corresponding to the preset driving parameters can be determined in the road condition data. The preset driving parameters may include distance, speed, acceleration, jerk, etc. The driving trajectory data may include the parameter values ​​of each driving parameter corresponding to the target vehicle at each moment. The road condition data includes the trajectory data of the environment vehicle within the preset distance range with the target vehicle. The driving trajectory data includes the parameter values ​​of each driving parameter corresponding to the environment vehicle at each moment. The server thus uses the self-supervised cost method to calculate the cost value corresponding to the simulated trajectory according to the parameter values ​​of each driving parameter of the target vehicle and the environment vehicle at each moment. The cost value corresponding to the simulated trajectory may be a weighted sum of multiple cost values. Specifically, the server determines multiple cost values ​​to be calculated according to the cost function in the self-supervised cost method, and then calculates the cost value corresponding to the simulated trajectory according to the parameter values ​​of each driving parameter of the target vehicle and the environment vehicle at each moment, the cost function, and the cost weight corresponding to each cost value. The cost weights corresponding to the multiple cost values ​​in the cost function can be used as trajectory planning parameters. For example, when the cost function is the weighted sum of the distance cost and the speed cost, if the weights corresponding to the distance cost and the speed cost need to be automatically adjusted, the weights corresponding to the two costs can be used as trajectory planning parameters. In this embodiment, by comparing the parameter values ​​of each driving parameter of the target vehicle and the environment vehicle within the preset distance range, it is possible to avoid a collision between the target vehicle and the environment vehicle, avoid the phenomenon of racing at the same speed, improve the accuracy of trajectory planning in the subsequent automatic driving process, and ensure the safety of automatic driving.

[0051] Step 208, adjust the current trajectory planning parameters according to the cost value to obtain the adjusted trajectory planning parameters, use the adjusted trajectory planning parameters as the current trajectory planning parameters, and return to the step of generating a simulated trajectory of the target vehicle according to the trajectory data, road condition data, road information, route data and the current trajectory planning parameters through the data simulation unit until the preset conditions are met, stop parameter adjustment, and obtain the target trajectory planning parameters.

[0052] The data planning unit includes multiple trajectory planning parameters, and the trajectory planning parameters can be determined according to the trajectory planning algorithm adopted by the data simulation unit. The trajectory planning algorithm needs to use a cost function for cost calculation. Therefore, the trajectory planning parameters can be determined according to the cost function. The trajectory planning parameters corresponding to different cost functions may be different. The server can set the data planning unit according to the trajectory planning parameters in the cost function so that the subsequent data planning unit can perform trajectory planning according to the trajectory planning parameters.

[0053] Before adjusting the trajectory parameters, the server can pre-specify the trajectory planning parameters and pre-set the parameter adjustment range of the trajectory planning parameters in a large number of trajectory planning parameters in the data planning unit. The pre-specified trajectory planning parameters are used as the current trajectory planning parameters. During the parameter adjustment process, after calculating the cost value corresponding to the simulated trajectory, the server can adjust the current trajectory planning parameters in the data simulation unit according to the cost value and the trajectory planning range, and use the adjusted trajectory planning parameters as the current trajectory planning parameters, and return to the step of generating the simulated trajectory of the target vehicle by the data simulation unit according to the trajectory data, road condition data, road information, route data and the current trajectory planning parameters, and repeat the parameter adjustment until the preset conditions are met. The preset condition can be that the current trajectory planning parameters have reached the optimal value within the parameter adjustment range. The optimal value is pre-stored in the server so that the server can adjust the parameters. The server can use the Bayesian method to generate the adjusted trajectory planning parameters according to the cost value, so that the adjusted trajectory planning parameters are used as the current trajectory planning parameters and input into the data simulation unit. When the current trajectory planning parameters meet the preset conditions, the server stops parameter adjustment and uses the trajectory planning parameters at this time as the target trajectory planning parameters. For example, when the current trajectory planning parameters are the weights of efficiency cost and safety cost, the server can iteratively adjust the weights of the two costs within the parameter adjustment range according to the cost value. When the weight of the efficiency cost and the weight of the safety cost both reach the optimal value, the parameter adjustment is stopped, and the weight of the efficiency cost and the weight of the safety cost at this time are used as the target trajectory planning parameters.

[0054] In one embodiment, the server may store the target trajectory planning parameters in the data simulation unit. During the autonomous driving process, the data simulation unit may use the target trajectory planning parameters to perform trajectory planning for the autonomous driving vehicle. Since the target trajectory planning parameters are pre-adjusted and accurate trajectory planning parameters, the accuracy of trajectory planning may be improved.

[0055] In this embodiment, the acquired trajectory data of the target vehicle within a preset time period, the road condition data within a preset distance range with the target vehicle, the road information corresponding to the target vehicle within the preset time period, and the route data of the target vehicle within the preset time period are input into the data simulation unit, and the data simulation unit generates the simulated trajectory of the target vehicle according to the trajectory data, road condition data, road information, route data and current trajectory planning parameters, thereby calculating the cost of the simulated trajectory, obtaining the cost value corresponding to the simulated trajectory, and then adjusting the current trajectory planning parameters according to the cost value, taking the adjusted trajectory planning parameters as the current trajectory planning parameters, and returning to the step of generating the simulated trajectory of the target vehicle according to the trajectory data, road condition data, road information, route data and current trajectory planning parameters by the data simulation unit, repeating the parameter adjustment until the preset conditions are met, stopping the parameter adjustment, and obtaining the target trajectory planning parameters. In the process of adjusting the trajectory planning parameters, the trajectory planning method of the trajectory planner is not concerned, and the parameters can be adjusted independently of the trajectory planner, so it can be applied to any trajectory planning method under various traffic scenarios, effectively improving the parameter accuracy of the trajectory planning algorithm. At the same time, only the current trajectory planning parameters and the preset conditions are needed to realize automatic adjustment of the parameters, which improves the adjustment efficiency of the trajectory planning algorithm parameters.

[0056] In one of the embodiments, the step of generating a simulated trajectory of a target vehicle according to trajectory data, road condition data, road information, route data and current trajectory planning parameters by a data simulation unit includes: extracting trajectory data of each frame from the trajectory data, extracting road condition data of each frame from the road condition data, extracting road information of each frame from the road information and extracting route data of each frame from the route data by the data simulation unit; generating a simulated trajectory segment of each frame according to the trajectory data of each frame, the road condition data of each frame, the road information of each frame, the route data of each frame and the current trajectory planning parameters; after obtaining the simulated trajectory segment of the previous frame, generating a simulated trajectory segment corresponding to the next frame according to the trajectory data of the next frame, the road condition data of the next frame, the road information of the next frame, the route data of the next frame and the current trajectory planning parameters, until the simulated trajectory segment of the last frame is obtained, and the simulated trajectory of the target vehicle is obtained according to the simulated trajectory segments of multiple frames.

[0057] Trajectory data may include the position, speed, acceleration, jerk, etc. of the target vehicle at each moment. Road condition data includes the position, speed, acceleration, jerk, etc. of the surrounding vehicles within a preset distance range from the target vehicle at each moment. Road information may include the traffic lanes, road boundary points, lane line information, etc. where the target vehicle is located. The road information may be obtained from a high-precision map. Route data may include the destination identification of the target vehicle, the location of the destination, etc. For example, the preset time period may be 40s. The preset distance range may be a range with a distance radius of 10m centered on the location of the target vehicle.

[0058] Since the trajectory data, road condition data, road information and route data sent by the server to the data simulation unit are all data within a preset time period, the data simulation unit can process the trajectory data, road condition data, road information and route data by frame to obtain multiple frames of data. Each frame of data includes the trajectory data, road condition data, road information and route data corresponding to the frame. Therefore, the data simulation unit extracts the data of each frame in turn according to the time sequence between each frame of data, and performs trajectory planning on the data of each frame according to the current trajectory planning parameters to obtain the trajectory planning result. The data simulation unit controls the target vehicle to run for a period of time according to the trajectory planning result until the timestamp corresponding to the next frame is reached, and the simulated trajectory segment corresponding to the frame is obtained. The simulated trajectory segment refers to the trajectory of the target vehicle controlled to run in each frame during the data simulation process. After generating the simulated trajectory segment of the previous frame, the data simulation unit obtains the data of the next frame, generates the simulated trajectory segment corresponding to the next frame according to the data of the next frame, until the simulated trajectory segment of the last frame is obtained, and then the data simulation unit obtains the simulated trajectory of the target vehicle according to the simulated trajectory segments of multiple frames. The data of the next frame includes the trajectory data of the next frame, the road condition data of the next frame, the road information of the next frame and the route data of the next frame. The simulation trajectory may include the position, speed, acceleration, jerk, etc. at each moment.

[0059] In one of the embodiments, for any traffic scenario, the number of simulation frames of the data simulation unit may be fixed, and thus the number of cycles for generating simulation segments is also fixed.

[0060] In this embodiment, a simulated trajectory segment of each frame is generated according to the trajectory data of each frame, the road condition data of each frame, the road information of each frame, the route data of each frame and the current trajectory planning parameters. After obtaining the simulated trajectory segment of the previous frame, the simulated trajectory segment corresponding to the next frame is generated according to the trajectory data of the next frame, the road condition data of the next frame, the road information of the next frame, the route data of the next frame and the current trajectory planning parameters, until the simulated trajectory segment of the last frame is obtained, and the simulated trajectory of the target vehicle is obtained according to the simulated trajectory segments of multiple frames. This is conducive to the subsequent calculation of the cost value according to the simulated trajectory to adjust the trajectory planning parameters.

[0061] In one embodiment, the data simulation unit includes a simulator and a trajectory planner, and the method further includes: sending trajectory data and road condition data to the simulator in the data simulation unit, and sending road information and route data to the trajectory planner in the data simulation unit, the trajectory planner including current trajectory planning parameters; extracting trajectory data of each frame from the trajectory data and road condition data of each frame from the road condition data by the simulator, and sending the extracted trajectory data of each frame and road condition data of each frame to the trajectory planner; extracting road information of each frame from the road information and route data of each frame from the route data by the trajectory planner, generating trajectory planning results corresponding to corresponding frames according to trajectory data of each frame, road condition data of each frame, road information of each frame, route data of each frame and current trajectory planning parameters, and sending the trajectory planning results corresponding to corresponding frames to the simulator; generating simulated trajectory segments of corresponding frames according to the trajectory planning results by the simulator.

[0062] The data simulation unit includes a simulator and a trajectory planner. The simulator is used to generate a simulated trajectory according to the trajectory planning result sent by the trajectory planner. The trajectory planner is used to generate a trajectory planning result corresponding to a corresponding frame according to the trajectory data of each frame, the road condition data of each frame, the road information of each frame, the route data of each frame and the current trajectory planning parameters sent by the simulator.

[0063] The simulator extracts the trajectory data of each frame in the trajectory data and the road condition data of each frame in the road condition data in chronological order, and sends the extracted data to the trajectory planner. The trajectory planner extracts the road information of the corresponding frame from the road information and the route data of the corresponding frame from the route data according to the received data, and can use the extracted trajectory data, road condition data, road information and route data of the same frame as a frame of data. The trajectory planner can generate the trajectory planning result corresponding to the corresponding frame according to the frame data and the current trajectory planning parameters, and send the trajectory planning result corresponding to the corresponding frame to the simulator. The simulator thus controls the target vehicle to travel according to the trajectory planning result until the timestamp corresponding to the next frame is reached. The simulator extracts the trajectory data of the next frame from the trajectory data and the road condition data of each frame from the road condition data, and sends the extracted trajectory data of the next frame and the road condition data of the next frame to the trajectory planner. The trajectory planner generates the trajectory planning result corresponding to the next frame according to the data of the next frame and the current trajectory planning parameters, and sends the trajectory planning result corresponding to the next frame to the simulator, so that the simulator controls the target vehicle to travel according to the trajectory planning result until the timestamp corresponding to the next frame is reached. The simulator and trajectory planner repeat the above steps of generating simulated trajectory segments until the last frame of simulated trajectory segments is obtained. Finally, the simulator outputs the simulated trajectory of the target vehicle based on multiple frames of simulated trajectory segments.

[0064] In this embodiment, multiple frames of simulated trajectory segments are generated through the loop between the simulator and the trajectory planner, and finally the simulated trajectory of the target vehicle is generated. In the process of adjusting the current trajectory planning parameters, the specific trajectory planning method of the trajectory planner is not concerned, and only the trajectory planning results output by the trajectory planner are needed to generate the simulated trajectory of the target vehicle. The parameter adjustment is independent of the trajectory planner, which can be applied to any trajectory planning method in a variety of traffic scenarios, and effectively improves the parameter accuracy of the trajectory planning algorithm.

[0065] In one embodiment, if Figure 3 As shown, the steps of adjusting the current trajectory planning parameters include:

[0066] Step 302: input the current trajectory planning parameters and the cost value into the non-gradient optimizer, and adjust the current trajectory planning parameters within the parameter adjustment range according to the cost value by the non-gradient optimizer to obtain the adjusted trajectory planning parameters, and send the adjusted trajectory planning parameters to the data simulation unit.

[0067] Step 304, the adjusted trajectory planning parameters are used as current trajectory planning parameters through the data simulation unit, and the process returns to the step of generating a simulated trajectory of the target vehicle according to the trajectory data, road condition data, road information, route data and current trajectory planning parameters through the data simulation unit until the preset conditions are met, and the parameter adjustment is stopped to obtain the target trajectory planning parameters.

[0068] The current trajectory planning parameters may be parameters that need to be adjusted that are pre-set by the server. After calculating the cost value of the simulated trajectory, the server obtains the current trajectory planning parameters of the data simulation unit. The data simulation unit includes a simulator and a trajectory planner, and the current trajectory planning parameters are the parameters of the trajectory planner. The server can call a non-gradient optimizer, input the current trajectory planning parameters and the cost value into the non-gradient optimizer, and use the Bayesian method to adjust the current trajectory planning parameters within the parameter adjustment range through the non-gradient optimizer to obtain the adjusted trajectory planning parameters. The non-gradient optimizer inputs the adjusted trajectory planning parameters into the data simulation unit, and the data simulation unit uses the adjusted trajectory planning parameters as the current trajectory planning parameters, and returns to the step of generating a simulated trajectory of the target vehicle according to trajectory data, road condition data, road information, route data and current trajectory planning parameters through the data simulation unit, repeating the parameter adjustment step until the preset conditions are met, stopping the parameter adjustment, and obtaining the target trajectory planning parameters. The preset condition may be that the current trajectory planning parameters have reached the optimal value within the parameter adjustment range.

[0069] In this embodiment, the current trajectory planning parameters are adjusted within the parameter adjustment range by a non-gradient optimizer, so that a new trajectory planning parameter can be quickly calculated, so that a simulation trajectory is generated again according to the new trajectory planning parameters, and the cost of the simulation trajectory is calculated to obtain the cost value corresponding to the simulation trajectory. The current trajectory planning parameters are adjusted within the parameter adjustment range by a non-gradient optimizer, and the target trajectory planning parameters are obtained through an iterative cycle.

[0070] In one embodiment, if Figure 4 As shown, a trajectory data processing device is provided, including: a data acquisition module 402, a data simulation module 404, a cost calculation module 406 and a parameter adjustment module 408, wherein:

[0071] The data acquisition module 402 is used to obtain the trajectory data of the target vehicle within a preset time period, the road condition data within a preset distance range from the target vehicle, the road information corresponding to the target vehicle within the preset time period, and the route data of the target vehicle within the preset time period.

[0072] The data simulation module 404 is used to input the trajectory data, traffic data, road information and route data into the data simulation unit, which includes the current trajectory planning parameters. The data simulation unit generates a simulated trajectory of the target vehicle based on the trajectory data, traffic data, road information, route data and the current trajectory planning parameters.

[0073] The cost calculation module 406 is used to perform cost calculation on the simulation trajectory to obtain a cost value corresponding to the simulation trajectory.

[0074] The parameter adjustment module 408 is used to adjust the current trajectory planning parameters according to the cost value to obtain the adjusted trajectory planning parameters, use the adjusted trajectory planning parameters as the current trajectory planning parameters, and return to the step of generating a simulated trajectory of the target vehicle according to the trajectory data, road condition data, road information, route data and the current trajectory planning parameters through the data simulation unit until the preset conditions are met, stop parameter adjustment, and obtain the target trajectory planning parameters.

[0075] In one embodiment, the data simulation module 404 is also used to extract the trajectory data of each frame from the trajectory data, extract the road condition data of each frame from the road condition data, extract the road information of each frame from the road information, and extract the route data of each frame from the route data through the data simulation unit; generate a simulated trajectory segment for each frame according to the trajectory data of each frame, the road condition data of each frame, the road information of each frame, the route data of each frame and the current trajectory planning parameters; after obtaining the simulated trajectory segment of the previous frame, generate the simulated trajectory segment corresponding to the next frame according to the trajectory data of the next frame, the road condition data of the next frame, the road information of the next frame, the route data of the next frame and the current trajectory planning parameters, until the simulated trajectory segment of the last frame is obtained, and the simulated trajectory of the target vehicle is obtained according to the simulated trajectory segments of multiple frames.

[0076] In one embodiment, the data simulation unit includes a simulator and a trajectory planner, and the data simulation module 404 is also used to send trajectory data and road condition data to the simulator in the data simulation unit, and send road information and route data to the trajectory planner in the data simulation unit, the trajectory planner including current trajectory planning parameters; extracting trajectory data of each frame from the trajectory data and road condition data of each frame from the road condition data by the simulator, and sending the extracted trajectory data of each frame and road condition data of each frame to the trajectory planner; extracting road information of each frame from the road information and route data of each frame from the route data by the trajectory planner, generating trajectory planning results corresponding to corresponding frames according to trajectory data of each frame, road condition data of each frame, road information of each frame, route data of each frame and current trajectory planning parameters, and sending the trajectory planning results corresponding to corresponding frames to the simulator; generating simulated trajectory segments of corresponding frames according to the trajectory planning results by the simulator.

[0077] In one embodiment, the cost calculation module 406 is further used to calculate the similarity between the trajectory data and the simulated trajectory; and obtain the cost value corresponding to the simulated trajectory according to the similarity calculation.

[0078] In one embodiment, the cost calculation module 406 is also used to determine the driving trajectory data corresponding to the preset driving parameters in the simulated trajectory; determine the driving road condition data corresponding to the preset driving parameters in the road condition data; and calculate the cost value corresponding to the simulated trajectory based on the driving trajectory data and the driving road condition data.

[0079] In one embodiment, the parameter adjustment module 408 is also used to input the current trajectory planning parameters and the cost value into the non-gradient optimizer, and the non-gradient optimizer adjusts the current trajectory planning parameters within the parameter adjustment range according to the cost value to obtain the adjusted trajectory planning parameters, and sends the adjusted trajectory planning parameters to the data simulation unit; the data simulation unit uses the adjusted trajectory planning parameters as the current trajectory planning parameters, and returns to the step of generating a simulated trajectory of the target vehicle according to the trajectory data, road condition data, road information, route data and the current trajectory planning parameters through the data simulation unit until the preset conditions are met, stops the parameter adjustment, and obtains the target trajectory planning parameters.

[0080] For the specific definition of the trajectory data processing device, please refer to the definition of the trajectory data processing method above, which will not be repeated here. Each module in the above-mentioned trajectory data processing device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0081] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, a communication interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer-readable instructions and a database. The internal memory provides an environment for the operation of the operating system and computer-readable instructions in the non-volatile storage medium. The database of the computer device is used to store data of a trajectory data processing method. The communication interface of the computer device is used to connect and communicate with an external terminal. When the computer-readable instructions are executed by the processor, a trajectory data processing method is implemented.

[0082] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0083] A computer device includes a memory and one or more processors. The memory stores computer-readable instructions. When the computer-readable instructions are executed by the one or more processors, the one or more processors execute the steps in the above-mentioned method embodiments.

[0084] One or more non-volatile computer-readable storage media storing computer-readable instructions, which, when executed by one or more processors, enable the one or more processors to perform the steps in the above-mentioned various method embodiments.

[0085] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through computer-readable instructions, and the above-mentioned computer-readable instructions can be stored in a non-volatile computer-readable storage medium. When the computer-readable instructions are executed, they may include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0086] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0087] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A trajectory data processing method, comprising: Acquire the trajectory data of the target vehicle within a preset time period, the road condition data within a preset distance range from the target vehicle, the road information corresponding to the target vehicle within the preset time period, and the route data of the target vehicle within the preset time period; Inputting the trajectory data, the road condition data, the road information and the route data into a data simulation unit, wherein the data simulation unit includes current trajectory planning parameters, and generating a simulated trajectory of the target vehicle according to the trajectory data, the road condition data, the road information, the route data and the current trajectory planning parameters by the data simulation unit; Performing cost calculation on the simulation trajectory to obtain a cost value corresponding to the simulation trajectory; and The current trajectory planning parameters are adjusted according to the cost value to obtain adjusted trajectory planning parameters, the adjusted trajectory planning parameters are used as the current trajectory planning parameters, and the step of generating the simulated trajectory of the target vehicle according to the trajectory data, the road condition data, the road information, the route data and the current trajectory planning parameters by the data simulation unit is returned until a preset condition is met, and the parameter adjustment is stopped to obtain the target trajectory planning parameters, including: Inputting the current trajectory planning parameters and the cost value into a non-gradient optimizer, adjusting the current trajectory planning parameters within a parameter adjustment range according to the cost value by the non-gradient optimizer to obtain adjusted trajectory planning parameters, and sending the adjusted trajectory planning parameters to the data simulation unit; and The adjusted trajectory planning parameters are used as the current trajectory planning parameters by the data simulation unit, and the step of generating the simulated trajectory of the target vehicle according to the trajectory data, the road condition data, the road information, the route data and the current trajectory planning parameters by the data simulation unit is returned to, until the preset conditions are met, the parameter adjustment is stopped, and the target trajectory planning parameters are obtained.

2. The method according to claim 1, characterized in that The step of generating the simulated trajectory of the target vehicle according to the trajectory data, the road condition data, the road information, the route data and the current trajectory planning parameters by the data simulation unit includes: Extracting, by the data simulation unit, trajectory data of each frame from the trajectory data, road condition data of each frame from the road condition data, road information of each frame from the road information, and route data of each frame from the route data; Generate a simulated trajectory segment for each frame according to the trajectory data for each frame, the road condition data for each frame, the road information for each frame, the route data for each frame and the current trajectory planning parameters; and After obtaining the simulated trajectory segment of the previous frame, the simulated trajectory segment corresponding to the next frame is generated according to the trajectory data of the next frame, the road condition data of the next frame, the road information of the next frame, the route data of the next frame and the current trajectory planning parameters, until the simulated trajectory segment of the last frame is obtained, and the simulated trajectory of the target vehicle is obtained according to the simulated trajectory segments of multiple frames.

3. The method according to claim 2, characterized in that The data simulation unit includes a simulator and a trajectory planner, and the method further includes: sending the trajectory data and the road condition data to a simulator in the data simulation unit, and sending the road information and the route data to a trajectory planner in the data simulation unit, wherein the trajectory planner includes the current trajectory planning parameters; Extracting the trajectory data of each frame from the trajectory data and the road condition data of each frame from the road condition data by the simulator, and sending the extracted trajectory data of each frame and the road condition data of each frame to the trajectory planner; Extracting the road information of each frame from the road information and the route data of each frame from the route data by the trajectory planner, generating a trajectory planning result corresponding to a corresponding frame according to the trajectory data of each frame, the road condition data of each frame, the road information of each frame, the route data of each frame and the current trajectory planning parameters, and sending the trajectory planning result corresponding to the corresponding frame to the simulator; and The simulator generates a simulated trajectory segment of a corresponding frame according to the trajectory planning result.

4. The method according to claim 1, characterized in that The performing cost calculation on the simulation trajectory to obtain a cost value corresponding to the simulation trajectory includes: Calculating the similarity between the trajectory data and the simulated trajectory; and The cost value corresponding to the simulated trajectory is obtained according to the similarity calculation.

5. The method according to claim 1, characterized in that The performing cost calculation on the simulation trajectory to obtain a cost value corresponding to the simulation trajectory includes: Determining driving trajectory data corresponding to preset driving parameters in the simulated trajectory; Determining the driving road condition data corresponding to the preset driving parameter in the road condition data; and The cost value corresponding to the simulated trajectory is calculated according to the driving trajectory data and the driving road condition data.

6. A trajectory data processing device, comprising: A data acquisition module, used to acquire the trajectory data of the target vehicle within a preset time period, the road condition data within a preset distance range from the target vehicle, the road information corresponding to the target vehicle within the preset time period, and the route data of the target vehicle within the preset time period; A data simulation module, used for inputting the trajectory data, the road condition data, the road information and the route data into a data simulation unit, wherein the data simulation unit includes current trajectory planning parameters, and generating a simulated trajectory of the target vehicle according to the trajectory data, the road condition data, the road information, the route data and the current trajectory planning parameters by the data simulation unit; A cost calculation module, used to perform cost calculation on the simulation trajectory to obtain a cost value corresponding to the simulation trajectory; and a parameter adjustment module, for adjusting the current trajectory planning parameters according to the cost value to obtain adjusted trajectory planning parameters, using the adjusted trajectory planning parameters as the current trajectory planning parameters, returning to the step of generating the simulated trajectory of the target vehicle according to the trajectory data, the road condition data, the road information, the route data and the current trajectory planning parameters by the data simulation unit until a preset condition is met, stopping parameter adjustment to obtain target trajectory planning parameters, and also for inputting the current trajectory planning parameters and the cost value into a non-gradient optimizer, adjusting the current trajectory planning parameters within a parameter adjustment range according to the cost value by the non-gradient optimizer to obtain adjusted trajectory planning parameters, and sending the adjusted trajectory planning parameters to the data simulation unit; And using the adjusted trajectory planning parameters as the current trajectory planning parameters through the data simulation unit, returning to the step of generating the simulated trajectory of the target vehicle through the data simulation unit according to the trajectory data, the road condition data, the road information, the route data and the current trajectory planning parameters, until the preset conditions are met, stopping the parameter adjustment, and obtaining the target trajectory planning parameters.

7. The device according to claim 6, characterized in that The data simulation module is also used to extract the trajectory data of each frame from the trajectory data, extract the road condition data of each frame from the road condition data, extract the road information of each frame from the road information, and extract the route data of each frame from the route data through the data simulation unit; Generate a simulated trajectory segment for each frame according to the trajectory data of each frame, the road condition data of each frame, the road information of each frame, the route data of each frame and the current trajectory planning parameters; and after obtaining the simulated trajectory segment of the previous frame, generate a simulated trajectory segment corresponding to the next frame according to the trajectory data of the next frame, the road condition data of the next frame, the road information of the next frame, the route data of the next frame and the current trajectory planning parameters, until the simulated trajectory segment of the last frame is obtained, and obtain the simulated trajectory of the target vehicle according to the simulated trajectory segments of multiple frames.

8. The device according to claim 6, characterized in that The cost calculation module is further used to calculate the similarity between the trajectory data and the simulated trajectory; and obtain the cost value corresponding to the simulated trajectory according to the similarity.

9. A computer device comprising a memory and one or more processors, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the one or more processors perform the following steps: Acquire the trajectory data of the target vehicle within a preset time period, the road condition data within a preset distance range from the target vehicle, the road information corresponding to the target vehicle within the preset time period, and the route data of the target vehicle within the preset time period; Inputting the trajectory data, the road condition data, the road information and the route data into a data simulation unit, wherein the data simulation unit includes current trajectory planning parameters, and generating a simulated trajectory of the target vehicle according to the trajectory data, the road condition data, the road information, the route data and the current trajectory planning parameters by the data simulation unit; Performing cost calculation on the simulation trajectory to obtain a cost value corresponding to the simulation trajectory; and The current trajectory planning parameters are adjusted according to the cost value to obtain adjusted trajectory planning parameters, the adjusted trajectory planning parameters are used as the current trajectory planning parameters, and the step of generating the simulated trajectory of the target vehicle according to the trajectory data, the road condition data, the road information, the route data and the current trajectory planning parameters by the data simulation unit is returned to until a preset condition is met, the parameter adjustment is stopped, and the target trajectory planning parameters are obtained; Inputting the current trajectory planning parameters and the cost value into a non-gradient optimizer, adjusting the current trajectory planning parameters within a parameter adjustment range according to the cost value by the non-gradient optimizer to obtain adjusted trajectory planning parameters, and sending the adjusted trajectory planning parameters to the data simulation unit; And using the adjusted trajectory planning parameters as the current trajectory planning parameters through the data simulation unit, returning to the step of generating the simulated trajectory of the target vehicle through the data simulation unit according to the trajectory data, the road condition data, the road information, the route data and the current trajectory planning parameters, until the preset conditions are met, stopping the parameter adjustment, and obtaining the target trajectory planning parameters.

10. The computer device according to claim 9, characterized in that When the processor executes the computer-readable instructions, the processor further performs the following steps: extracting, by the data simulation unit, trajectory data of each frame from the trajectory data, extracting road condition data of each frame from the road condition data, extracting road information of each frame from the road information, and extracting route data of each frame from the route data; Generate a simulated trajectory segment for each frame according to the trajectory data of each frame, the road condition data of each frame, the road information of each frame, the route data of each frame and the current trajectory planning parameters; and after obtaining the simulated trajectory segment of the previous frame, generate a simulated trajectory segment corresponding to the next frame according to the trajectory data of the next frame, the road condition data of the next frame, the road information of the next frame, the route data of the next frame and the current trajectory planning parameters, until the simulated trajectory segment of the last frame is obtained, and obtain the simulated trajectory of the target vehicle according to the simulated trajectory segments of multiple frames.

11. The computer device according to claim 10, characterized in that The data simulation unit includes a simulator and a trajectory planner, and the processor further performs the following steps when executing the computer-readable instructions: sending the trajectory data and the road condition data to the simulator in the data simulation unit, and sending the road information and the route data to the trajectory planner in the data simulation unit, wherein the trajectory planner includes the current trajectory planning parameters; extracting the trajectory data of each frame from the trajectory data and the road condition data of each frame from the road condition data by the simulator, and sending the extracted trajectory data of each frame and the road condition data of each frame to the trajectory planner; extracting the road information of each frame from the road information and the route data of each frame from the route data by the trajectory planner, generating a trajectory planning result corresponding to a corresponding frame according to the trajectory data of each frame, the road condition data of each frame, the road information of each frame, the route data of each frame and the current trajectory planning parameters, and sending the trajectory planning result corresponding to the corresponding frame to the simulator; And generating a simulated trajectory segment of a corresponding frame according to the trajectory planning result through the simulator.

12. The computer device according to claim 9, characterized in that When the processor executes the computer-readable instructions, the processor further performs the following steps: calculating the similarity between the trajectory data and the simulated trajectory; and calculating the cost value corresponding to the simulated trajectory according to the similarity.

13. One or more non-volatile computer-readable storage media storing computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to perform the following steps: Acquire the trajectory data of the target vehicle within a preset time period, the road condition data within a preset distance range from the target vehicle, the road information corresponding to the target vehicle within the preset time period, and the route data of the target vehicle within the preset time period; Inputting the trajectory data, the road condition data, the road information and the route data into a data simulation unit, wherein the data simulation unit includes current trajectory planning parameters, and generating a simulated trajectory of the target vehicle according to the trajectory data, the road condition data, the road information, the route data and the current trajectory planning parameters by the data simulation unit; Performing cost calculation on the simulation trajectory to obtain a cost value corresponding to the simulation trajectory; and The current trajectory planning parameters are adjusted according to the cost value to obtain adjusted trajectory planning parameters, the adjusted trajectory planning parameters are used as the current trajectory planning parameters, and the step of generating the simulated trajectory of the target vehicle according to the trajectory data, the road condition data, the road information, the route data and the current trajectory planning parameters by the data simulation unit is returned to until a preset condition is met, the parameter adjustment is stopped, and the target trajectory planning parameters are obtained; Inputting the current trajectory planning parameters and the cost value into a non-gradient optimizer, adjusting the current trajectory planning parameters within a parameter adjustment range according to the cost value by the non-gradient optimizer to obtain adjusted trajectory planning parameters, and sending the adjusted trajectory planning parameters to the data simulation unit; And using the adjusted trajectory planning parameters as the current trajectory planning parameters through the data simulation unit, returning to the step of generating the simulated trajectory of the target vehicle through the data simulation unit according to the trajectory data, the road condition data, the road information, the route data and the current trajectory planning parameters, until the preset conditions are met, stopping the parameter adjustment, and obtaining the target trajectory planning parameters.

14. The storage medium according to claim 13, characterized in that When the computer readable instructions are executed by the processor, the following steps are further performed: extracting, by the data simulation unit, trajectory data of each frame from the trajectory data, extracting road condition data of each frame from the road condition data, extracting road information of each frame from the road information, and extracting route data of each frame from the route data; Generate a simulated trajectory segment for each frame according to the trajectory data of each frame, the road condition data of each frame, the road information of each frame, the route data of each frame and the current trajectory planning parameters; and after obtaining the simulated trajectory segment of the previous frame, generate a simulated trajectory segment corresponding to the next frame according to the trajectory data of the next frame, the road condition data of the next frame, the road information of the next frame, the route data of the next frame and the current trajectory planning parameters, until the simulated trajectory segment of the last frame is obtained, and obtain the simulated trajectory of the target vehicle according to the simulated trajectory segments of multiple frames.

15. The storage medium according to claim 14, characterized in that The data simulation unit includes a simulator and a trajectory planner, and the computer-readable instructions further perform the following steps when executed by the processor: sending the trajectory data and the road condition data to the simulator in the data simulation unit, and sending the road information and the route data to the trajectory planner in the data simulation unit, wherein the trajectory planner includes the current trajectory planning parameters; extracting the trajectory data of each frame from the trajectory data and the road condition data of each frame from the road condition data by the simulator, and sending the extracted trajectory data of each frame and the road condition data of each frame to the trajectory planner; extracting the road information of each frame from the road information and the route data of each frame from the route data by the trajectory planner, generating a trajectory planning result corresponding to a corresponding frame according to the trajectory data of each frame, the road condition data of each frame, the road information of each frame, the route data of each frame and the current trajectory planning parameters, and sending the trajectory planning result corresponding to the corresponding frame to the simulator; And generating a simulated trajectory segment of a corresponding frame according to the trajectory planning result through the simulator.

16. The storage medium according to claim 13, characterized in that When the computer-readable instructions are executed by the processor, the following steps are further performed: calculating the similarity between the trajectory data and the simulated trajectory; and calculating the cost value corresponding to the simulated trajectory according to the similarity.

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