A setting method, device, equipment and program product of traffic scene simulation
By acquiring and adjusting the simulated vehicle trajectory and combining it with dynamics co-simulation, traffic scene simulations are automatically constructed, solving the inefficiency problem caused by manually setting parameters in existing technologies and improving simulation efficiency and accuracy.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- 苏州万集车联网技术有限公司
- Filing Date
- 2024-12-02
- Publication Date
- 2026-06-02
AI Technical Summary
Existing technologies require manually setting a large number of parameters when setting up traffic scenario simulations, resulting in long simulation preparation times, low efficiency, and reduced efficiency of traffic scenario simulation testing.
By acquiring a preset set of simulated vehicle trajectories, adjusting the simulated vehicle trajectory of the test vehicle according to the type of simulated event, generating the target vehicle trajectory, and determining the behavioral trajectory of traffic participants based on dynamic co-simulation, a traffic scenario simulation is constructed.
It improves the efficiency of traffic scenario simulation setup and testing, reduces the steps of manual parameter setting, and enhances the accuracy and realism of the simulation.
Smart Images
Figure CN122133299A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of traffic scene simulation technology, and in particular to a method, apparatus, equipment and program product for setting up traffic scene simulation. Background Technology
[0002] With the rapid development of intelligent transportation system technology, creating traffic system simulations to simulate road traffic scenarios has become an important way to simulate and analyze road traffic scenarios.
[0003] Current technical solutions require technicians to manually set parameters such as the starting point, speed, and path of the test vehicle for each simulated event type. This simulates the vehicle's behavior on the road corresponding to the simulated event type, such as congestion or lane changes. The solution then determines how these behaviors affect other road users, deduces their behavioral trajectories, and thus establishes the corresponding traffic scenario simulation. However, this approach is inefficient due to the need for manually setting numerous parameters and the lengthy simulation preparation time, leading to low efficiency in determining traffic scenario simulations and reducing the overall efficiency of traffic scenario simulation testing.
[0004] Therefore, how to improve the efficiency of setting up traffic scenario simulations and improving the efficiency of traffic scenario simulation testing are technical problems that need to be solved by those skilled in the art. Summary of the Invention
[0005] The purpose of this application is to provide a method, apparatus, terminal device, computer-readable storage medium, and computer program product for setting up traffic scene simulation, aiming to improve the efficiency of setting up traffic scene simulation and the efficiency of traffic scene simulation testing.
[0006] Firstly, this application provides a method for setting up a traffic scene simulation. The method includes:
[0007] Obtain a preset set of simulated vehicle trajectories;
[0008] Determine the type of simulated event, and determine the test vehicle from the set of simulated vehicle trajectories based on the type of simulated event;
[0009] The simulated vehicle trajectory of the test vehicle is adjusted according to the simulated event type to obtain the target vehicle trajectory, and the behavioral trajectories of traffic participants are determined based on the target vehicle trajectory to obtain the traffic scene simulation.
[0010] In one embodiment, adjusting the simulated vehicle trajectory of the test vehicle according to the simulated event type to obtain the target vehicle trajectory, and determining the behavioral trajectories of traffic participants based on the target vehicle trajectory to obtain the traffic scene simulation, includes:
[0011] Based on dynamic co-simulation, the simulated vehicle trajectory of the test vehicle is adjusted according to the simulated event type to obtain the target vehicle trajectory, and the behavioral trajectories of traffic participants are determined based on the target vehicle trajectory to obtain the traffic scene simulation.
[0012] In one embodiment, obtaining the preset set of simulated vehicle trajectories includes:
[0013] Obtain real vehicle driving data;
[0014] The real vehicle driving data is mapped onto the simulated road network to obtain a preset set of simulated vehicle trajectories.
[0015] In one embodiment, the method further includes:
[0016] Traffic anomaly detection is performed based on the traffic scenario simulation, and the detection results are determined.
[0017] The accuracy of the traffic scenario simulation is determined based on the detection results and the simulated event type.
[0018] In one embodiment, determining the type of simulated event and determining the test vehicle from the set of simulated vehicle trajectories based on the type of simulated event includes:
[0019] Determine the type of simulated event, and determine the test vehicle from the set of simulated vehicle trajectories based on the type of simulated event and the vehicle type.
[0020] In one embodiment, the method further includes:
[0021] Determine noise data; the noise data includes noise type and noise parameters corresponding to the noise type;
[0022] Obtain the trajectory of the target vehicle and add the noise data to the trajectory of the target vehicle.
[0023] Secondly, this application also provides a device for simulating traffic scenarios. The device includes:
[0024] The acquisition module is used to acquire a preset set of simulated vehicle trajectories;
[0025] A determination module is used to determine the type of simulated event and determine the test vehicle from the set of simulated vehicle trajectories based on the type of simulated event.
[0026] The setting module is used to adjust the simulated vehicle trajectory of the test vehicle according to the simulated event type to obtain the target vehicle trajectory, and determine the behavior trajectory of traffic participants based on the target vehicle trajectory to obtain a traffic scene simulation.
[0027] Thirdly, this application also provides a terminal device. The terminal device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above.
[0028] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method described above.
[0029] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of the method described above.
[0030] This application provides a method for setting up traffic scene simulation. The method adjusts the simulated vehicle trajectory of a pre-set test vehicle according to the simulated event type to obtain the target vehicle trajectory. Based on the target vehicle trajectory, the behavioral trajectories of traffic participants are determined to obtain the traffic scene simulation. In other words, this method generalizes from the simulated vehicle trajectory of the test vehicle to obtain the traffic scene simulation, eliminating the need for technicians to manually set numerous parameters for each simulated event type. Therefore, this method improves the efficiency of setting up and testing traffic scene simulations.
[0031] It is understood that the traffic scene simulation setting device, terminal equipment, computer-readable storage medium and computer program product provided in the embodiments of this application have the same beneficial effects as the traffic scene simulation setting method described above, and will not be repeated here. Attached Figure Description
[0032] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0033] Figure 1 A flowchart illustrating a method for setting up a traffic scene simulation, as provided in an embodiment of this application;
[0034] Figure 2 This is the trajectory of the target vehicle when the test vehicle changes lanes in one embodiment of this application;
[0035] Figure 3 This is the target vehicle trajectory corresponding to the test vehicle moving slowly in one embodiment of this application;
[0036] Figure 4 This is the trajectory of the target vehicle when the test vehicle is illegally parked, as shown in one embodiment of this application.
[0037] Figure 5 This is the trajectory of the target vehicle when the test vehicle experiences moderate traffic congestion in one embodiment of this application;
[0038] Figure 6 This is the trajectory of the target vehicle when the test vehicle experiences severe traffic congestion in one embodiment of this application;
[0039] Figure 7 This is a schematic diagram illustrating the process of mapping real vehicle driving data to simulation software to obtain a simulated vehicle trajectory corresponding to the real vehicle driving data in another embodiment of this application.
[0040] Figure 8 A comparative illustration of adding noise data to the target vehicle trajectory before and after in another embodiment of this application;
[0041] Figure 9 A schematic diagram of the structure of a traffic scene simulation device provided in an embodiment of this application;
[0042] Figure 10 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. Detailed Implementation
[0043] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of this application with unnecessary detail.
[0044] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.
[0045] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.
[0046] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."
[0047] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.
[0048] References to "one embodiment" or "some embodiments" in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized. "A plurality" means "two or more."
[0049] The present application provides a method for setting up a traffic scene simulation, which can be executed by the processor of a terminal device when running a corresponding computer program.
[0050] Figure 1 The flowchart illustrates a traffic scene simulation setup method provided in this embodiment. For ease of explanation, only the parts relevant to this embodiment are shown. The method provided in this embodiment includes the following steps:
[0051] S100: Obtain the preset set of simulated vehicle trajectories.
[0052] The simulated vehicle trajectory set includes at least one simulated vehicle trajectory; a simulated vehicle trajectory refers to the trajectory of a simulated vehicle traveling on a traffic road.
[0053] The simulated vehicle trajectory in this step can be obtained by mapping the trajectory of a real vehicle, or it can be determined by multiple trajectory points manually set by technicians in the simulation software. This embodiment does not limit this.
[0054] S200: Determine the type of simulated event and identify the test vehicle from the set of simulated vehicle trajectories based on the type of simulated event.
[0055] The simulated event type refers to the specific traffic event type that the test vehicle is expected to simulate. Simulated event types include lane change congestion, illegal parking, slow-moving traffic, obstacles, reversing, driving against traffic, pedestrians crossing the road, lane obstruction, accidents, and construction. The test vehicle is the vehicle used to simulate the specific traffic event corresponding to the simulated event type.
[0056] In this step, after determining the type of simulated event, one or more test vehicles are selected from a preset set of simulated vehicle trajectories. Specifically, when selecting test vehicles, parameters such as the initial position, direction of travel, and speed of the corresponding vehicle can be determined based on each simulated vehicle trajectory, and the test vehicle is selected based on these parameters; the test vehicle can reasonably simulate a specific traffic event.
[0057] S300: Adjust the simulated vehicle trajectory of the test vehicle according to the simulated event type to obtain the target vehicle trajectory, and determine the behavior trajectory of traffic participants based on the target vehicle trajectory to obtain the traffic scene simulation.
[0058] Specifically, based on the characteristics of the simulated event type, the simulated vehicle trajectory of the selected test vehicle is adjusted. For example, if the simulated event type is lane change, the simulated vehicle trajectory needs to be adjusted. The adjusted simulated vehicle trajectory performs the lane change operation on a specific road segment; this adjusted simulated vehicle trajectory is the target vehicle trajectory, i.e., the vehicle trajectory used to simulate a specific traffic event. Figure 2 This is the trajectory of the target vehicle when the test vehicle changes lanes in one embodiment of this application; Figure 3 This is the target vehicle trajectory corresponding to the test vehicle moving slowly in one embodiment of this application; Figure 4 This is the trajectory of the target vehicle when the test vehicle is illegally parked, as shown in one embodiment of this application. Figure 5 This is the trajectory of the target vehicle when the test vehicle experiences moderate congestion in one embodiment of this application; Figure 6 This is the trajectory of the target vehicle when the test vehicle experiences severe traffic congestion in one embodiment of this application.
[0059] Understandably, when the simulated trajectory of the test vehicle changes, the behavioral trajectories of corresponding traffic participants, such as other vehicles and pedestrians, will also be adjusted according to the target vehicle's trajectory. Specifically, the behavioral trajectories of traffic participants are determined based on the target vehicle's trajectory, traffic rules, and road conditions.
[0060] By combining the trajectory of the target vehicle and the behavioral trajectories of traffic participants, a complete traffic scenario simulation is constructed. In other words, the traffic scenario simulation will simulate the impact of test vehicles on the surrounding traffic flow when a specific traffic event occurs.
[0061] This application provides a traffic scene simulation method that adjusts the simulated vehicle trajectory of a preset test vehicle according to the simulated event type to obtain the target vehicle trajectory, and determines the behavior trajectory of traffic participants based on the target vehicle trajectory to obtain a traffic scene simulation. In other words, this method generalizes the simulated vehicle trajectory of the test vehicle to obtain a traffic scene simulation, without requiring technicians to manually set a large number of parameters for each simulated event type. Therefore, this method can improve the efficiency of setting up traffic scene simulations.
[0062] Based on the above embodiments, this embodiment further explains and optimizes the technical solution. Specifically, in this embodiment, the simulated vehicle trajectory of the test vehicle is adjusted according to the simulated event type to obtain the target vehicle trajectory, and the behavioral trajectories of traffic participants are determined based on the target vehicle trajectory to obtain the traffic scene simulation, including:
[0063] Based on dynamic co-simulation, the simulated vehicle trajectory of the test vehicle is adjusted according to the type of simulated event to obtain the target vehicle trajectory. Then, the behavioral trajectories of traffic participants are determined based on the target vehicle trajectory to obtain the traffic scene simulation.
[0064] Specifically, based on dynamic co-simulation, the simulated vehicle trajectory of the test vehicle is first determined. The simulation platform then adjusts the simulated vehicle trajectory according to the type of simulated event, outputting the target vehicle trajectory of the test vehicle. The target vehicle trajectory is then sent to the simulation software. The simulation software deduces the behavioral trajectories of traffic participants associated with the test vehicle based on the target vehicle trajectory.
[0065] In one specific embodiment, co-simulation is performed using the simulation software TESS NG (Traffic Event Simulation and Network Generation, a tool or framework for traffic event simulation and network generation) and the simulation platform Carla. This involves integrating a dynamics module into the simulation process, adding dynamic characteristics to the test vehicle to achieve software-in-the-loop testing. Specifically, the simulation platform Carla outputs simulated driving data of the test vehicle and updates the corresponding target vehicle trajectory, sending the adjusted target vehicle trajectory to the simulation software TESS NG. TESS NG determines the behavioral trajectories of traffic participants based on the target vehicle trajectory and sends these trajectories back to the simulation platform Carla. The simulation platform Carla further adjusts the test vehicle's trajectory based on the traffic participant's behavioral trajectories, further updating the target vehicle trajectory. This cyclical interaction between the simulation software TESS NG and the simulation platform Carla ensures that the dynamic interaction between the test vehicle and traffic participants conforms to the requirements of real-world traffic scenarios.
[0066] According to the dynamic co-simulation method in this embodiment, the impact of traffic events on traffic flow can be simulated more accurately, improving the realism and effectiveness of traffic scenario simulation.
[0067] Based on the above embodiments, this embodiment further explains and optimizes the technical solution. Specifically, in this embodiment, obtaining a preset set of simulated vehicle trajectories includes:
[0068] Obtain real vehicle driving data;
[0069] Real vehicle driving data is mapped onto a simulated road network to obtain a preset set of simulated vehicle trajectories.
[0070] Specifically, real vehicle driving data can be collected through traffic monitoring systems, vehicle-mounted GPS devices, or other sensor devices. Real vehicle driving data includes information such as timestamps, driving speed, driving direction, and acceleration corresponding to the vehicle's location (real geographic coordinates).
[0071] Then, a road network model similar to the real world is built in the simulation software, which is called a simulated road network. The simulated road network includes roads, traffic signs, traffic lights, etc., to accurately reflect the traffic environment of the real world.
[0072] The real vehicle driving data is then mapped onto the simulated road network. Specifically, the real geographic coordinates are converted into pixel coordinates in the simulated road network to obtain the corresponding trajectory points. The trajectory points are then connected in a preset order to obtain the simulated vehicle trajectory, which is to form the corresponding simulated vehicle trajectory in the simulated road network.
[0073] It should be noted that the simulated vehicle trajectory set includes at least one simulated vehicle trajectory. Mapping multiple sets of real vehicle driving data will yield multiple simulated vehicle trajectories; that is, the simulated vehicle trajectory set includes multiple simulated vehicle trajectories.
[0074] In practical applications, after obtaining real vehicle driving data, further data cleaning and preprocessing can be performed. These operations remove outliers from the real vehicle driving data, ensuring the accuracy of the simulated vehicle trajectories mapped to the simulated road network.
[0075] Figure 7This is a schematic diagram illustrating the process of mapping real vehicle driving data to simulation software to obtain a simulated vehicle trajectory corresponding to the real vehicle driving data, as described in another embodiment of this application. In a specific example, base station sensing trajectory data (eventDetectData.csv) is obtained through a traffic monitoring system, and real vehicle driving data, including vehicle ID, latitude and longitude, driving speed, heading angle, vehicle type, and vehicle size, is extracted from the base station sensing trajectory data. After preprocessing the real vehicle driving data, it is saved as JSON format data. The preprocessed JSON format real vehicle driving data is read and mapped to Tessng. The simulated vehicle trajectory is set in Tessng and then managed by Tessng.
[0076] In this embodiment, the simulated vehicle trajectory is determined based on real vehicle driving data, resulting in a more accurate simulated vehicle trajectory and thus improving the accuracy of traffic scene simulation.
[0077] Based on the above embodiments, this embodiment further explains and optimizes the technical solution. Specifically, in this embodiment, a method for setting up a traffic scene simulation further includes:
[0078] Traffic anomaly detection is performed based on traffic scenario simulation, and the detection results are determined.
[0079] The accuracy of traffic scenario simulations is determined based on the test results and the types of simulated events.
[0080] Traffic anomaly detection refers to the application of anomaly detection algorithms to identify and label events or behaviors in traffic scenario simulations that do not conform to expected behavioral patterns. In this embodiment, traffic anomaly detection is performed based on the traffic scenario simulation to determine the detection results; then, the detection results are compared with simulated event types to check whether the simulation results match the expected event types, and the accuracy of the traffic scenario simulation is determined based on the degree of matching. That is, if the detection results match the expected results (simulated event types), it indicates that the accuracy of the traffic scenario simulation is high; otherwise, it indicates that the accuracy of the traffic scenario simulation is low.
[0081] In a specific embodiment, multiple simulated event types and corresponding simulated occurrence locations and simulated occurrence events can be set. Correspondingly, traffic anomaly detection is performed based on traffic scene simulation, and the detection results are determined. The detection results include the number of detected anomalies, the detection location, and the detection time. The detection results are compared with the simulated event types, simulated occurrence locations, and simulated occurrence events to determine the accuracy of the traffic scene simulation.
[0082] For example, suppose the simulated event types set for the test vehicle are illegal parking, driving without following lane guidance, and motor vehicles occupying non-motorized vehicle lanes; when performing traffic anomaly detection on the traffic scenario simulation, if the detection result is: the test vehicle has abnormal events such as illegal parking, driving without following lane guidance, and motor vehicles occupying non-motorized vehicle lanes, then it means that the traffic scenario simulation is accurate.
[0083] In one test case, assume the pre-determined simulated event type is slow-moving traffic, meaning the expected result is reporting slow-moving vehicles. The test steps are as follows:
[0084] 1. The actual minimum speed limit in the tunnel is 35 km / h, and there is no congestion.
[0085] 2. Simulated scenario: The vehicle target appears at any position in lane 1 within the range of base station 1 in tunnel 1. After standing still for 3 seconds, it accelerates to 35km / h within 3 seconds and drives at a constant speed until it leaves the tunnel.
[0086] The process of setting up the behavioral modeling of the test vehicle is as follows:
[0087] A car is generated at position 1 of station 1, with a speed of 0 for 3 seconds; at the 3rd second, the acceleration is set to 3.24 m / s². 2 The vehicle continues for 3 seconds; at the 6th second, it maintains a speed of 35 km / h and exits the tunnel; the vehicle cannot change lanes.
[0088] The behavior modeling process for the background vehicle is as follows:
[0089] Map the real trajectory to the simulation software, select the trajectory of a simulated vehicle driving normally, and set the minimum driving speed to be greater than 35 km / h.
[0090] In another test case, assume the pre-determined simulated event type is slow-moving, meaning the expected result is reporting a slow-moving vehicle. The test steps are as follows:
[0091] 1. The actual minimum speed limit in the tunnel is 35 km / h, and there is no congestion.
[0092] 2. Simulated scenario: The vehicle target appears at any position in lane 1 within the range of base station 1 in tunnel 1. After standing still for 1.5 seconds, it accelerates to 35km / h within 3 seconds and drives at a constant speed until it leaves the tunnel.
[0093] The process of setting up the behavioral modeling of the test vehicle is as follows:
[0094] A car is generated at position 1 of station 1, with a speed of 0 and a duration of 1.5 seconds; at the 1.5-second mark, the acceleration is set to 3.24 m / s². 2 The vehicle continues for 3 seconds; at the 4.5-second mark, it maintains a speed of 35 km / h and exits the tunnel; the vehicle cannot change lanes.
[0095] The behavior modeling process for the background vehicle is as follows:
[0096] Map the real trajectory to the simulation software, select the trajectory of a simulated vehicle driving normally, and set the minimum driving speed to be greater than 35 km / h.
[0097] In another test case, assume the pre-determined simulated event type is slow-moving, meaning the expected outcome is no reporting of slow-moving vehicles. The test steps are as follows:
[0098] 1. The actual minimum speed limit in the tunnel is 35 km / h, and there is no congestion.
[0099] 2. Simulated scenario: The vehicle target appears at any position in lane 1 within the range of base station 1 in tunnel 1. After standing still for 3 seconds, it accelerates to 35km / h within 1.5 seconds and drives at a constant speed until it leaves the tunnel.
[0100] The process of setting up the behavioral modeling of the test vehicle is as follows:
[0101] A car is generated at position 1 of station 1, with a speed of 0 for 3 seconds; at the 3rd second, the acceleration is set to 3.24 m / s². 2 The vehicle will remain in this position for 1.5 seconds; at the 4.5-second mark, it will maintain a speed of 35 km / h and exit the tunnel; the vehicle is not allowed to change lanes.
[0102] The behavior modeling process for the background vehicle is as follows:
[0103] Map the real trajectory to the simulation software, select the trajectory of a simulated vehicle driving normally, and set the minimum driving speed to be greater than 35 km / h.
[0104] According to the test cases above, if the actual test results are the same as the expected results (simulated event types), it means that the traffic scenario simulation is accurate; otherwise, it means that the traffic scenario simulation is inaccurate.
[0105] According to the method of this embodiment, it is possible to further verify whether the traffic scene simulation is accurate, so that the traffic scene simulation can be corrected when it is determined that the traffic scene simulation is inaccurate, thereby improving the accuracy of simulation tests based on the traffic scene simulation.
[0106] Based on the above embodiments, this embodiment further explains and optimizes the technical solution. Specifically, in this embodiment, determining the simulated event type and determining the test vehicle from the simulated vehicle trajectory set according to the simulated event type includes:
[0107] Determine the type of simulated event, and then select the test vehicle from the set of simulated vehicle trajectories based on the type of simulated event and the vehicle type.
[0108] In this embodiment, when selecting a test vehicle, the vehicle driving parameters corresponding to the simulated vehicle trajectory are first obtained. Based on the vehicle driving parameters, it is determined whether the corresponding test vehicle can reasonably simulate a specific traffic event corresponding to the simulated event type. The vehicle driving parameters include driving speed, acceleration, driving direction, driving path, etc.
[0109] If multiple test vehicles can reasonably simulate a specific traffic event corresponding to the simulated event type, then the vehicle type of the test vehicles is further obtained. The test vehicles are then determined from the simulated vehicle trajectory set based on the simulated event type and vehicle type. The vehicle type can be classified according to characteristics such as vehicle purpose, size, and shape; for example, vehicle types can include cars, trucks, buses, and motorcycles.
[0110] In this embodiment, the test vehicle is determined from the set of simulated vehicle trajectories based on the type of simulated event and the type of vehicle. This ensures that the test vehicle can accurately reflect the vehicle behavior in the real world, thereby improving the accuracy and reliability of traffic scene simulation.
[0111] Based on the above embodiments, this embodiment further explains and optimizes the technical solution. Specifically, in this embodiment, a method for setting up a traffic scene simulation further includes:
[0112] Determine the noise data; the noise data includes the noise type and the noise parameters corresponding to the noise type.
[0113] Obtain the trajectory of the target vehicle and add noise data to the trajectory.
[0114] Noise types include size variation, backtracking, type variation, frame drops, sudden changes in heading angle, sudden changes in vertical direction, forward lurching, vertical jitter, jitter before and after a stopped vehicle, and ID jumps. For each noise type, corresponding noise parameters can be set. For example, for the noise type of sudden changes in heading angle, the direction and change value of the heading angle change can be set; different noise parameters result in different noise effects.
[0115] In addition, noise types can also include random data loss, such as the random loss of 10 frames of data, and random speed fluctuations, such as the speed of 10 frames of data fluctuating upwards by 10 km / h.
[0116] In a specific embodiment, after determining the noise data and obtaining the target vehicle trajectory, noise data is added to the target vehicle trajectory. For example... Figure 8 The diagram shown is a comparison of adding noise data to the target vehicle trajectory before and after in another embodiment of this application.
[0117] According to the method in this embodiment, by adding noise data to the target vehicle trajectory, the target vehicle trajectory can be made closer to the complexity and uncertainty of the real world, thereby improving the realism and reliability of the simulation.
[0118] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.
[0119] It should be noted that the information collection process (such as the facial image collection process, fingerprint information collection process, etc.) / feature extraction process involved in this application is carried out with the user's knowledge and permission. That is, the information collection process / feature extraction process complies with the requirements of laws and regulations and does not constitute an act that harms the public interest.
[0120] Figure 9 The diagram shown is a structural schematic of a traffic scene simulation device provided in an embodiment of this application. Figure 9 As shown, a traffic scene simulation setup device according to this embodiment includes an acquisition module 910, a determination module 920, and a setting module 930; wherein,
[0121] The acquisition module 910 is used to acquire a preset set of simulated vehicle trajectories;
[0122] The determination module 920 is used to determine the type of simulated event and to determine the test vehicle from the set of simulated vehicle trajectories based on the type of simulated event.
[0123] The setting module 930 is used to adjust the simulated vehicle trajectory of the test vehicle according to the simulated event type to obtain the target vehicle trajectory, and determine the behavior trajectory of traffic participants based on the target vehicle trajectory to obtain the traffic scene simulation.
[0124] The traffic scene simulation setting device provided in this application embodiment has the same beneficial effects as the traffic scene simulation setting method described above.
[0125] In one embodiment, the setting module 930 includes:
[0126] The co-simulation setup submodule is used to adjust the simulated vehicle trajectory of the test vehicle according to the type of simulated event based on dynamic co-simulation, obtain the target vehicle trajectory, and determine the behavior trajectory of traffic participants based on the target vehicle trajectory to obtain the traffic scene simulation.
[0127] In one embodiment, the acquisition module 910 includes:
[0128] The driving data acquisition submodule is used to acquire real vehicle driving data;
[0129] The mapping submodule is used to map real vehicle driving data onto the simulated road network to obtain a preset set of simulated vehicle trajectories.
[0130] In one embodiment, a traffic scene simulation setup device further includes:
[0131] The anomaly detection module is used to detect traffic anomalies based on traffic scenario simulation and determine the detection results.
[0132] The detection result determination module is used to determine the accuracy of traffic scenario simulation based on the detection results and the type of simulated event.
[0133] In one embodiment, the determining module 920 includes:
[0134] The test vehicle determination submodule is used to determine the type of simulated event and to determine the test vehicle from the set of simulated vehicle trajectories based on the type of simulated event and the vehicle type.
[0135] In one embodiment, a traffic scene simulation setup device further includes:
[0136] The noise setting module is used to determine noise data; the noise data includes the noise type and the noise parameters corresponding to the noise type.
[0137] Obtain the trajectory of the target vehicle and add noise data to the trajectory.
[0138] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.
[0139] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.
[0140] Figure 10This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. Figure 10 As shown, the terminal device 1000 of this embodiment includes a memory 1001, a processor 1002, and a computer program 1003 stored in the memory 1001 and executable on the processor 1002; when the processor 1002 executes the computer program 1003, it implements the steps in the above-mentioned traffic scenario simulation setting method embodiments; or when the processor 1002 executes the computer program 1003, it implements the functions of each module / unit in the above-mentioned device embodiments.
[0141] For example, computer program 1003 can be divided into one or more modules / units, and one or more modules / units are stored in memory 1001 and executed by processor 1002 to implement the method of the embodiments of this application. One or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of computer program 1003 in terminal device 1000. For example, computer program 1003 can be divided into an acquisition module, a determination module, and a setting module, with the specific functions of each module as follows:
[0142] The acquisition module is used to acquire a preset set of simulated vehicle trajectories;
[0143] The determination module is used to determine the type of simulated event and to identify the test vehicle from the set of simulated vehicle trajectories based on the type of simulated event.
[0144] The setting module is used to adjust the simulated vehicle trajectory of the test vehicle according to the simulated event type to obtain the target vehicle trajectory, and determine the behavior trajectory of traffic participants based on the target vehicle trajectory to obtain the traffic scene simulation.
[0145] In applications, terminal device 1000 can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. Terminal device 1000 may include, but is not limited to, memory 1001 and processor 1002. Those skilled in the art will understand that... Figure 10 This is merely an example of a terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than shown, or combine certain components, or different components. For example, a terminal device may also include input / output devices, network access devices, buses, etc.; among which, input / output devices may include cameras, audio acquisition / playback devices, displays, etc.; network access devices may include communication modules for wireless communication with external devices.
[0146] In applications, the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.
[0147] In applications, memory can be an internal storage unit of a terminal device, such as its hard drive or RAM; it can also be an external storage device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card; or it can include both internal and external storage units. Memory is used to store operating systems, applications, boot loaders, data, and other programs, such as computer program code. Memory can also be used to temporarily store data that has been output or will be output.
[0148] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement the steps in the above-described method embodiments.
[0149] This application implements all or part of the processes in the methods of the above embodiments, which can be accomplished by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to a terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, such as a USB flash drive, a portable hard drive, a magnetic disk, or an optical disk.
[0150] The computer-readable storage medium provided in this application embodiment has the same beneficial effects as the above-described method for setting up a traffic scene simulation.
[0151] This application also provides a computer program product, including a computer program that, when executed by a processor, can implement the steps in the various method embodiments described above.
[0152] The computer program product provided in this application embodiment has the same beneficial effects as the above-described method for setting up a traffic scene simulation.
[0153] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.
[0154] Those skilled in the art will recognize that the device and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0155] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interface, or the device may be indirectly coupled or communicated, and may be electrical, mechanical, or other forms.
[0156] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.
Claims
1. A method for setting up a traffic scene simulation, characterized in that, The method includes: Obtain a preset set of simulated vehicle trajectories; Determine the type of simulated event, and determine the test vehicle from the set of simulated vehicle trajectories based on the type of simulated event; The simulated vehicle trajectory of the test vehicle is adjusted according to the simulated event type to obtain the target vehicle trajectory, and the behavioral trajectories of traffic participants are determined based on the target vehicle trajectory to obtain the traffic scene simulation.
2. The method according to claim 1, characterized in that, The step of adjusting the simulated vehicle trajectory of the test vehicle according to the simulated event type to obtain the target vehicle trajectory, and determining the behavioral trajectories of traffic participants based on the target vehicle trajectory to obtain a traffic scene simulation includes: Based on dynamic co-simulation, the simulated vehicle trajectory of the test vehicle is adjusted according to the simulated event type to obtain the target vehicle trajectory, and the behavioral trajectories of traffic participants are determined based on the target vehicle trajectory to obtain the traffic scene simulation.
3. The method according to claim 1, characterized in that, The acquisition of the preset set of simulated vehicle trajectories includes: Obtain real vehicle driving data; The real vehicle driving data is mapped onto the simulated road network to obtain a preset set of simulated vehicle trajectories.
4. The method according to claim 1, characterized in that, The method further includes: Traffic anomaly detection is performed based on the traffic scenario simulation, and the detection results are determined. The accuracy of the traffic scenario simulation is determined based on the detection results and the simulated event type.
5. The method according to claim 1, characterized in that, The step of determining the type of simulated event and determining the test vehicle from the set of simulated vehicle trajectories based on the type of simulated event includes: Determine the type of simulated event, and determine the test vehicle from the set of simulated vehicle trajectories based on the type of simulated event and the vehicle type.
6. The method according to any one of claims 1 to 5, characterized in that, The method further includes: Determine noise data; the noise data includes noise type and noise parameters corresponding to the noise type; Obtain the trajectory of the target vehicle and add the noise data to the trajectory of the target vehicle.
7. A device for simulating traffic scenarios, characterized in that, The device includes: The acquisition module is used to acquire a preset set of simulated vehicle trajectories; A determination module is used to determine the type of simulated event and determine the test vehicle from the set of simulated vehicle trajectories based on the type of simulated event. The setting module is used to adjust the simulated vehicle trajectory of the test vehicle according to the simulated event type to obtain the target vehicle trajectory, and determine the behavior trajectory of traffic participants based on the target vehicle trajectory to obtain a traffic scene simulation.
8. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 6.
9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 6.
10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.