Problem scenario extraction method, device, equipment and medium for traffic flow simulation

Abstract

This invention discloses a method, apparatus, device, and medium for extracting problem scenarios in traffic flow simulation. The method includes: acquiring real traffic flow data, and generating a continuous simulation test scenario and a traffic flow simulation model based on the real traffic flow data; then, running the traffic flow simulation model in the continuous simulation test scenario; when a vehicle alarm information corresponding to the test vehicle simulation model is detected by a preset vehicle alarm detection method, the current test scenario is determined as a problem scenario, and the vehicle driving parameters corresponding to the problem scenario are obtained. The technical solution of this embodiment, by employing a preset vehicle alarm detection method, can achieve rapid identification and location of problem scenarios during continuous traffic flow testing, thereby improving the efficiency of finding problem scenarios in traffic flow simulation testing.

Description

Technical Field

[0001] The present invention relates to the field of autonomous driving technology, and in particular to a method, device, equipment and medium for extracting problem scenarios for traffic flow simulation. Background Art

[0002] With the rapid development of automobile digitization and intelligence, the driving environment conditions such as roads, surrounding traffic and weather conditions that smart cars need to deal with are becoming more complex, and the driving environment has strong uncertainty and unpredictability. Therefore, simulation test research on key scenarios in traffic flow is of great significance to improving the safety of autonomous driving.

[0003] At present, existing traffic flow simulation test research mainly adopts large-scale continuous traffic flow simulation test, which can realize traffic flow test closer to real scenes. However, in the existing technology, the continuously changing scenes and long simulation process make it difficult to identify and extract the problem scenes when the test vehicles have abnormal interactive behaviors, making it difficult to quickly identify and locate the problem scenes. Summary of the invention

[0004] The present invention provides a method, device, equipment and medium for extracting problem scenes in traffic flow simulation, which can realize rapid identification and positioning of problem scenes during continuous traffic flow testing, and can improve the efficiency of searching for problem scenes during traffic flow simulation testing.

[0005] According to one aspect of the present invention, a method for extracting problem scenarios for traffic flow simulation is provided, comprising:

[0006] Acquire real traffic flow data, and generate a continuous simulation test scenario and a traffic flow simulation model based on the real traffic flow data;

[0007] Wherein, the traffic flow simulation model includes at least one vehicle simulation model, and the vehicle simulation model includes a test vehicle simulation model;

[0008] Running the traffic flow simulation model in the continuous simulation test scenario;

[0009] When the vehicle warning information corresponding to the test vehicle simulation model is detected by a preset vehicle warning detection method, the current test scene is determined as a problem scene, and the vehicle driving parameters corresponding to the problem scene are obtained.

[0010] According to another aspect of the present invention, there is provided a problem scene extraction device for traffic flow simulation, comprising:

[0011] A traffic flow simulation model generation module is used to obtain real traffic flow data and generate a continuous simulation test scenario and a traffic flow simulation model based on the real traffic flow data;

[0012] Wherein, the traffic flow simulation model includes at least one vehicle simulation model, and the vehicle simulation model includes a test vehicle simulation model;

[0013] A traffic flow simulation model running module, used for running the traffic flow simulation model in the continuous simulation test scenario;

[0014] The problem scenario determination module is used to determine the current test scenario as a problem scenario when the vehicle warning information corresponding to the test vehicle simulation model is detected by a preset vehicle warning detection method, and obtain the vehicle driving parameters corresponding to the problem scenario.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the problem scenario extraction method for traffic flow simulation described in any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the problem scenario extraction method for traffic flow simulation described in any embodiment of the present invention when executed.

[0020] The technical solution of the embodiment of the present invention obtains real traffic flow data and generates a continuous simulation test scene and a traffic flow simulation model based on the real traffic flow data; then, the traffic flow simulation model is run in the continuous simulation test scene, and when the vehicle alarm information corresponding to the test vehicle simulation model is detected by a preset vehicle alarm detection method, the current test scene is determined as a problem scene, and the vehicle driving parameters corresponding to the problem scene are obtained. By using the preset vehicle alarm detection method to detect the vehicle alarm of the test vehicle, the problem scene can be quickly identified and located during the continuous traffic flow test, and the efficiency of finding the problem scene during the traffic flow simulation test can be improved.

[0021] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 is a flow chart of a problem scenario extraction method for traffic flow simulation provided in accordance with the first embodiment of the present invention;

[0024] Figure 2A is a flow chart of a method for extracting problem scenarios in traffic flow simulation provided in accordance with Embodiment 2 of the present invention;

[0025] Figure 2B is a flow chart of a problem scenario extraction method for traffic flow simulation provided in accordance with the second embodiment of the present invention;

[0026] Figure 3 is a schematic diagram of the structure of a problem scene extraction device for traffic flow simulation provided by Embodiment 3 of the present invention;

[0027] Figure 4 It is a structural schematic diagram of an electronic device for implementing the problem scene extraction method for traffic flow simulation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first", "second", "target", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] Embodiment 1

[0031] Figure 1 A flowchart of a method for extracting problem scenarios in traffic flow simulation is provided for the first embodiment of the present invention. This embodiment is applicable to the case where problem scenarios are extracted during a continuous traffic flow simulation test. The method can be executed by a problem scenario extraction device for traffic flow simulation. The problem scenario extraction device for traffic flow simulation can be implemented in the form of hardware and / or software. The problem scenario extraction device for traffic flow simulation can be configured in an electronic device. Typically, the electronic device can be a computer device or a server. Figure 1 As shown, the method includes:

[0032] S110, acquiring real traffic flow data, and generating a continuous simulation test scenario and a traffic flow simulation model according to the real traffic flow data.

[0033] The real traffic flow data may be actual survey data of traffic flow in real scenes; for example, the real traffic flow data may include vehicle driving data, driving road data, driving environment data, etc. The traffic flow may be a flow of vehicles formed by continuous driving of vehicles on the road.

[0034] In this embodiment, the real traffic flow data can be obtained by collecting the driving behavior data of the driver during the actual driving process. Optionally, the real traffic flow data can also be obtained through Internet of Vehicles data (for example, interaction data between different vehicles).

[0035] In this embodiment, after obtaining the real traffic flow data, a continuous simulation test scenario can be built according to the real traffic flow data through the simulation software for active safety and autonomous driving, and a traffic flow simulation model can be constructed. The continuous simulation test scenario can be a simulation of a continuous vehicle driving scenario, and can be composed of a series of static simulation test scenarios.

[0036] Among them, the traffic flow simulation model can be a simulation model corresponding to a vehicle traveling on the road. The traffic flow simulation model can include at least one vehicle simulation model, and the vehicle simulation model can include a test vehicle simulation model. Optionally, the vehicle simulation model can also include a background vehicle simulation model. In this embodiment, the traffic flow simulation model can include a test vehicle simulation model and multiple background vehicle simulation models. The number of background vehicle simulation models and the corresponding vehicle models can be adaptively set according to scene requirements.

[0037] S120: running the traffic flow simulation model in the continuous simulation test scenario.

[0038] In this embodiment, after the construction of the continuous simulation test scene and the traffic flow simulation model is completed, the traffic flow simulation model can be automatically run in the continuous simulation test scene through the simulation software. Specifically, the test vehicle simulation model can simulate automatic driving in the continuous simulation test scene, and include different background vehicle simulation models in different static simulation test scenes. At the same time, the driving parameters of the test vehicle simulation model and each background vehicle simulation model, such as vehicle speed, vehicle acceleration, etc., can be output through the simulation software.

[0039] S130. When vehicle warning information corresponding to the test vehicle simulation model is detected by a preset vehicle warning detection method, the current test scene is determined as a problem scene, and vehicle driving parameters corresponding to the problem scene are obtained.

[0040] The preset vehicle alarm detection method may be a pre-set method for detecting vehicle alarm information. In this embodiment, a corresponding vehicle alarm detection method may be pre-set according to the vehicle alarm logic. The vehicle alarm logic may be a pre-set logical condition for the test vehicle to generate alarm information.

[0041] Specifically, when the vehicle alarm information corresponding to the test vehicle simulation model is detected by the preset vehicle alarm detection method, it means that the test vehicle simulation model triggers an alarm in the current test scenario, and the current test scenario can be determined as a problem scenario. Further, the vehicle driving parameters corresponding to each vehicle simulation model included in the problem scenario can be output and stored by the simulation software.

[0042] Among them, the vehicle driving parameters can be used to verify the problem scenario. Optionally, the vehicle driving parameters may include at least one of the vehicle speed, vehicle acceleration, vehicle collision time, and vehicle spacing. In this embodiment, by extracting the vehicle driving parameters corresponding to the problem scenario, the problem scenario can be avoided in the actual scenario of autonomous driving, thereby improving the safety of autonomous driving.

[0043] In this embodiment, by setting a preset vehicle alarm detection method according to the vehicle alarm logic, the preparatory identification of vehicle alarm information can be achieved, thereby achieving rapid and accurate identification of problem scenarios, overcoming the difficulty in extracting problem scenarios during long-term and large-scale traffic flow simulations, and improving the efficiency of simulation testing and scenario research.

[0044] The technical solution of the embodiment of the present invention obtains real traffic flow data and generates a continuous simulation test scene and a traffic flow simulation model based on the real traffic flow data; then, the traffic flow simulation model is run in the continuous simulation test scene, and when the vehicle alarm information corresponding to the test vehicle simulation model is detected by a preset vehicle alarm detection method, the current test scene is determined as a problem scene, and the vehicle driving parameters corresponding to the problem scene are obtained. By using the preset vehicle alarm detection method to detect the vehicle alarm of the test vehicle, the problem scene can be quickly identified and located during the continuous traffic flow test, and the efficiency of finding the problem scene during the traffic flow simulation test can be improved.

[0045] In an optional implementation of this embodiment, after running the traffic flow simulation model in the continuous simulation test scenario, the following may also be included:

[0046] Acquire at least one vehicle driving parameter corresponding to each of the vehicle simulation models during the operation of the traffic flow simulation model, as well as a parameter acquisition time corresponding to each of the vehicle driving parameters, and generate a mapping relationship between the vehicle simulation model, the vehicle driving parameter and the parameter acquisition time and store it in a temporary cache.

[0047] In this embodiment, the vehicle driving parameters during the whole simulation test of the traffic flow simulation model can be outputted by the simulation software to obtain the vehicle driving parameters of each vehicle simulation model at different times. In addition, for the obtained vehicle driving parameters, a mapping relationship between the vehicle simulation model, the vehicle driving parameters and the parameter acquisition time can be generated, and the mapping relationship can be stored in a temporary cache. Thus, after the simulation test is completed, a large number of mapping relationships can be stored in the temporary cache.

[0048] Correspondingly, obtaining the vehicle driving parameters corresponding to the problem scenario may include:

[0049] Acquire the local system time corresponding to the problem scenario, and acquire at least one abnormal vehicle simulation model corresponding to the problem scenario and the abnormal vehicle driving parameters corresponding to each abnormal vehicle simulation model from the temporary cache according to the local system time corresponding to the problem scenario and the mapping relationship between the vehicle simulation model, the vehicle driving parameters and the parameter acquisition time;

[0050] Each of the abnormal vehicle simulation models and the corresponding abnormal vehicle driving parameters are stored in a local database, and the mapping relationship between the vehicle simulation model, the vehicle driving parameters and the parameter acquisition time in the temporary cache is deleted.

[0051] The local system time corresponding to the problem scenario may be the operating system time when the problem scenario is determined.

[0052] Specifically, after the simulation test is completed, the local system time corresponding to the identified problem scenario can be used to compare the parameter acquisition time of a series of mapping relationships in the temporary cache one by one to find the parameter acquisition time that is the same as the local system time. After that, the vehicle simulation model in the mapping relationship corresponding to the parameter acquisition time can be determined as an abnormal vehicle simulation model, and the vehicle driving parameters in the mapping relationship can be determined as abnormal vehicle driving parameters.

[0053] Furthermore, after obtaining the abnormal vehicle simulation model and the corresponding abnormal vehicle driving parameters, the abnormal vehicle simulation model and the corresponding abnormal vehicle driving parameters can be stored in a local database for long-term storage. At the same time, all the mapping relationships stored in the temporary cache can be deleted.

[0054] The advantage of the above setting is that it can avoid the occupation of storage space by a large number of vehicle driving parameters in normal test scenarios, which can greatly save data storage space and thus reduce the cost of traffic flow simulation testing.

[0055] In another optional implementation of the present embodiment, the test vehicle simulation model includes at least one sensor simulation model, and running the traffic flow simulation model in the continuous simulation test scenario may include: obtaining driving environment parameters corresponding to the test vehicle simulation model through each of the sensor simulation models, and when it is detected that the driving environment parameters corresponding to the test vehicle simulation model meet preset vehicle alarm conditions, generating vehicle alarm information corresponding to the test vehicle simulation model.

[0056] In this embodiment, the test vehicle simulation model may further include multiple sensor simulation models for real-time detection of driving environment parameters of the test vehicle simulation model. Optionally, the sensor simulation model may include at least one of a camera simulation model, a laser radar sensor simulation model, an ultrasonic radar sensor simulation model, and a millimeter wave radar sensor simulation model.

[0057] The driving environment parameters may include obstacle information, lane boundaries, and the distance to nearby vehicles. The preset vehicle warning condition is pre-set condition information for determining whether to issue a vehicle warning. For example, the preset vehicle warning condition may be that the distance to nearby vehicles is less than a preset vehicle distance threshold. In this embodiment, the type of driving environment parameters is not specifically limited.

[0058] Specifically, during the simulation test of the test vehicle simulation model, the test vehicle simulation model can detect the driving environment in real time through a preset sensor simulation model to obtain driving environment parameters. When the test vehicle simulation model detects that the acquired driving environment parameters meet the preset vehicle warning conditions, the corresponding vehicle warning information can be generated.

[0059] Embodiment 2

[0060] Figure 2A This is a flow chart of a method for extracting problem scenarios in traffic flow simulation provided by Embodiment 2 of the present invention. This embodiment is a further refinement of the above technical solution. The technical solution in this embodiment can be combined with one or more of the above implementations. Figure 2A As shown, the method includes:

[0061] S210: Acquire real traffic flow data, and generate a continuous simulation test scenario and a traffic flow simulation model based on the real traffic flow data.

[0062] In an optional implementation of the present embodiment, generating a traffic flow simulation model based on the real traffic flow data may include: obtaining test requirements, and obtaining traffic flow setting parameters based on the test requirements and the real traffic flow data; generating a traffic flow simulation model based on the traffic flow setting parameters.

[0063] The test requirements may include test time, test vehicle model, test weather, etc. In this embodiment, the test requirements and real traffic flow data may be combined to jointly determine the corresponding traffic flow setting parameters. Furthermore, a traffic flow simulation model may be generated by simulation software according to the determined traffic flow setting parameters.

[0064] The traffic flow setting parameters may be construction parameters of the traffic flow simulation model. Optionally, the traffic flow setting parameters may include at least one of the test vehicle model, the test vehicle driver style, weather parameters, the number of background vehicles, the background vehicle model, and the background vehicle driver style.

[0065] S220: Run the traffic flow simulation model in the continuous simulation test scenario.

[0066] S230, obtaining at least one vehicle driving parameter corresponding to each of the vehicle simulation models during the operation of the traffic flow simulation model, and a parameter acquisition time corresponding to each of the vehicle driving parameters, and generating a mapping relationship between the vehicle simulation model, the vehicle driving parameter and the parameter acquisition time and storing it in a temporary cache.

[0067] S240, performing segmented video recording of the operation process of the traffic flow simulation model to obtain at least one segmented video file, and a start time and an end time corresponding to each segmented video file, and storing each segmented video file and the corresponding start time and end time in the temporary cache.

[0068] In this embodiment, while testing the traffic flow simulation model, the entire test process can also be recorded in segmented video. Specifically, a segmented video file can be generated at each interval of the segmented video duration according to the preset segmented video duration. Thus, after the traffic flow simulation model test is completed, multiple segmented video files with the same duration corresponding to the test process can be obtained. Secondly, since the duration corresponding to each segmented video file is equal, the start time and end time corresponding to each segmented video file can be obtained at the same time.

[0069] In addition, after a segmented video file is obtained, the segmented video file and the corresponding start time and end time can be stored in a temporary cache to achieve temporary storage of the recorded video. The start time and end time corresponding to the segmented video file can be the recording time of the first frame of the video image and the last frame of the video image, respectively.

[0070] Optionally, the operation process of the traffic flow simulation model can be recorded in segmented video by a pre-written script to obtain multiple segmented video files, as well as the start time and end time corresponding to each segmented video file. Specifically, the start time, segmented video duration, and segmented video file name of the segmented video recording can be set in advance by a pre-written script, and then the pre-written script can be run to obtain multiple segmented video files corresponding to the entire test process.

[0071] Optionally, the segmented video recorded by the pre-written script can switch between the driver's perspective and the vehicle's perspective, and can fully display the image information of dynamic and static scenes around the test simulation vehicle model, which can improve the comprehensiveness of the video recording.

[0072] The advantage of the above setting is that it overcomes the problems of untimely manual video recording and inconsistent sizes of recorded video files, thereby greatly improving the research efficiency of related scenarios.

[0073] S250. Obtain the local system time corresponding to the problem scenario, and based on the local system time corresponding to the problem scenario and the mapping relationship between the vehicle simulation model, vehicle driving parameters and parameter acquisition time, obtain at least one abnormal vehicle simulation model corresponding to the problem scenario and the abnormal vehicle driving parameters corresponding to each of the abnormal vehicle simulation models from the temporary cache.

[0074] S260: storing each of the abnormal vehicle simulation models and the corresponding abnormal vehicle driving parameters in a local database, and deleting the mapping relationship between the vehicle simulation model, the vehicle driving parameters and the parameter acquisition time in the temporary cache.

[0075] S270: Obtain a target segmented video file corresponding to the problem scene from the temporary cache according to the local system time corresponding to the problem scene and the start time and end time corresponding to each segmented video file in the temporary cache.

[0076] In this embodiment, after the simulation test is finished, the target segmented video file corresponding to the local system time can also be obtained from the temporary cache according to the determined local system time corresponding to the problem scenario. For example, if it is detected that the local system time is between the start time and the end time of a segmented video file, the segmented video file can be determined as the target segmented video file corresponding to the problem scenario.

[0077] Optionally, before searching for the corresponding target segmented video file according to the local system time corresponding to the problem scene, the local system time may be verified according to the vehicle driving parameters corresponding to the problem scene and the parameter acquisition time corresponding to each vehicle driving parameter in the temporary cache. If the local system time successfully passes the verification, the corresponding target segmented video file is acquired according to the local system time.

[0078] S280: Store the target segmented video files corresponding to the problem scene in a local database, and delete other segmented video files in the temporary cache.

[0079] Further, after determining the target segmented video file, the target segmented video file can be stored in a local database, so that the target segmented video file is stored for a long time. In addition, for other segmented video files except the target segmented video file in the temporary buffer, then it can be deleted from the temporary buffer.

[0080] It is understandable that when multiple problem scenes are successfully identified, the above method can be used to obtain the vehicle driving parameters and target segmented video files corresponding to each problem scene, and store the vehicle driving parameters and target segmented video files corresponding to each problem scene in the local database. This embodiment does not specifically limit the data storage format of the local database.

[0081] The technical solution of the embodiment of the present invention is that after running the traffic flow simulation model in the continuous simulation test scene, the running process of the traffic flow simulation model is firstly recorded in segmented video to obtain multiple segmented video files, and the start time and end time corresponding to each segmented video file, and each segmented video file and the corresponding start time and end time are stored in a temporary cache; thereafter, when the vehicle alarm information corresponding to the test vehicle simulation model is detected by the preset vehicle alarm detection method, the current test scene is determined as a problem scene, and the vehicle driving parameters corresponding to the problem scene are obtained; further, according to the local system time corresponding to the problem scene, and the start time and end time corresponding to each segmented video file in the temporary cache, the target segmented video file corresponding to the problem scene is obtained from the temporary cache; finally, the target segmented video file corresponding to the problem scene is stored in the local database, and other segmented video files in the temporary cache are deleted; by recording the traffic flow simulation test process in segmented video, and screening and long-term storage of the target segmented video files corresponding to the problem scene, the standardization of the recorded video files can be improved, thereby improving the research efficiency of the problem scene, and further saving storage space.

[0082] In a specific implementation of this embodiment, the process of the problem scene extraction method for traffic flow simulation can be as follows: Figure 2B As shown. Specifically, first, a continuous simulation test scenario is built and a traffic flow simulation model is run. Then, a preset vehicle alarm detection method is used to identify and extract the problem scenario. Furthermore, the operation process of the traffic flow simulation model is recorded in segmented video to obtain multiple segmented video files. Finally, according to the identification results of the problem scenario, the vehicle driving parameters and segmented video files corresponding to the problem scenario are screened and retained.

[0083] Embodiment 3

[0084] Figure 3 This is a schematic diagram of the structure of a problem scene extraction device for traffic flow simulation provided by the third embodiment of the present invention. Figure 3 As shown, the device includes: a traffic flow simulation model generation module 310, a traffic flow simulation model operation module 320 and a problem scenario determination module 330; wherein,

[0085] The traffic flow simulation model generation module 310 is used to obtain real traffic flow data and generate a continuous simulation test scenario and a traffic flow simulation model according to the real traffic flow data;

[0086] Wherein, the traffic flow simulation model includes at least one vehicle simulation model, and the vehicle simulation model includes a test vehicle simulation model;

[0087] A traffic flow simulation model running module 320, used to run the traffic flow simulation model in the continuous simulation test scenario;

[0088] The problem scenario determination module 330 is used to determine the current test scenario as a problem scenario when the vehicle warning information corresponding to the test vehicle simulation model is detected by a preset vehicle warning detection method, and obtain the vehicle driving parameters corresponding to the problem scenario.

[0089] The technical solution of the embodiment of the present invention obtains real traffic flow data and generates a continuous simulation test scene and a traffic flow simulation model based on the real traffic flow data; then, the traffic flow simulation model is run in the continuous simulation test scene, and when the vehicle alarm information corresponding to the test vehicle simulation model is detected by a preset vehicle alarm detection method, the current test scene is determined as a problem scene, and the vehicle driving parameters corresponding to the problem scene are obtained. By using the preset vehicle alarm detection method to detect the vehicle alarm of the test vehicle, the problem scene can be quickly identified and located during the continuous traffic flow test, and the efficiency of finding the problem scene during the traffic flow simulation test can be improved.

[0090] Optionally, the problem scene extraction device for traffic flow simulation further includes:

[0091] A vehicle driving parameter acquisition module, used to acquire at least one vehicle driving parameter corresponding to each of the vehicle simulation models during the operation of the traffic flow simulation model, and a parameter acquisition time corresponding to each of the vehicle driving parameters, and generate a mapping relationship between the vehicle simulation model, the vehicle driving parameter and the parameter acquisition time and store it in a temporary cache;

[0092] The problem scenario determination module 330 includes:

[0093] an abnormal vehicle driving parameter acquisition unit, configured to acquire the local system time corresponding to the problem scenario, and acquire at least one abnormal vehicle simulation model corresponding to the problem scenario and the abnormal vehicle driving parameters corresponding to each abnormal vehicle simulation model from the temporary cache according to the local system time corresponding to the problem scenario and the mapping relationship between the vehicle simulation model, the vehicle driving parameters and the parameter acquisition time;

[0094] The abnormal vehicle driving parameter storage unit is used to store each abnormal vehicle simulation model and the corresponding abnormal vehicle driving parameter in a local database, and delete the mapping relationship between the vehicle simulation model, vehicle driving parameter and parameter acquisition time in the temporary cache.

[0095] Optionally, the problem scene extraction device for traffic flow simulation further includes:

[0096] A segmented video file acquisition module is used to perform segmented video recording of the operation process of the traffic flow simulation model to obtain at least one segmented video file, and a start time and an end time corresponding to each segmented video file, and store each segmented video file and the corresponding start time and end time in the temporary cache;

[0097] A target segmented video file acquisition module, used to acquire the target segmented video file corresponding to the problem scene from the temporary cache according to the local system time corresponding to the problem scene and the start time and end time corresponding to each segmented video file in the temporary cache;

[0098] The target segmented video file storage module is used to store the target segmented video file corresponding to the problem scene in the local database and delete other segmented video files in the temporary cache.

[0099] Optionally, the test vehicle simulation model includes at least one sensor simulation model, and the traffic flow simulation model operation module 320 includes:

[0100] The vehicle warning information generating unit is used to obtain the driving environment parameters corresponding to the test vehicle simulation model through each of the sensor simulation models, and generate vehicle warning information corresponding to the test vehicle simulation model when it is detected that the driving environment parameters corresponding to the test vehicle simulation model meet the preset vehicle warning conditions.

[0101] Optionally, the traffic flow simulation model generation module 310 includes:

[0102] A traffic flow setting parameter acquisition unit, used to acquire test requirements, and acquire traffic flow setting parameters according to the test requirements and the real traffic flow data;

[0103] The traffic flow simulation model generating unit is used to generate a traffic flow simulation model according to the traffic flow setting parameters.

[0104] Optionally, the vehicle driving parameter includes at least one of vehicle speed, vehicle acceleration, vehicle collision time and vehicle spacing.

[0105] Optionally, the traffic flow setting parameters include at least one of a test vehicle model, a test vehicle driver style, weather parameters, the number of background vehicles, the background vehicle model and the background vehicle driver style.

[0106] The problem scenario extraction device for traffic flow simulation provided by the embodiment of the present invention can execute the problem scenario extraction method for traffic flow simulation provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0107] It should be noted that in the technical solution of this embodiment, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0108] Embodiment 4

[0109] Figure 4 A schematic diagram of the structure of an electronic device 40 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0110] like Figure 4 As shown, the electronic device 40 includes at least one processor 41, and a memory connected to the at least one processor 41, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 to the random access memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other through a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0111] A number of components in the electronic device 40 are connected to the I / O interface 45, including: an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a disk, an optical disk, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0112] The processor 41 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 41 executes the various methods and processes described above, such as a problem scenario extraction method for traffic flow simulation.

[0113] In some embodiments, the problem scenario extraction method for traffic flow simulation can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 48. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 40 via the ROM 42 and / or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the problem scenario extraction method for traffic flow simulation described above can be performed. Alternatively, in other embodiments, the processor 41 can be configured to perform the problem scenario extraction method for traffic flow simulation in any other appropriate manner (for example, by means of firmware).

[0114] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0115] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0116] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0117] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0118] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0119] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0120] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0121] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for extracting problem scenarios for traffic flow simulation, characterized in that: include: Acquire real traffic flow data, and generate a continuous simulation test scenario and a traffic flow simulation model based on the real traffic flow data; Wherein, the traffic flow simulation model includes at least one vehicle simulation model, and the vehicle simulation model includes a test vehicle simulation model; Running the traffic flow simulation model in the continuous simulation test scenario; Acquire at least one vehicle driving parameter corresponding to each of the vehicle simulation models during the operation of the traffic flow simulation model, and a parameter acquisition time corresponding to each of the vehicle driving parameters, and generate a mapping relationship between the vehicle simulation model, the vehicle driving parameter and the parameter acquisition time and store it in a temporary cache; Performing segmented video recording of the operation process of the traffic flow simulation model to obtain at least one segmented video file, and a start time and an end time corresponding to each segmented video file, and storing each segmented video file and the corresponding start time and end time in the temporary cache; When the vehicle warning information corresponding to the test vehicle simulation model is detected by the preset vehicle warning detection method, the current test scene is determined as a problem scene, and the vehicle driving parameters corresponding to the problem scene are obtained; According to the local system time corresponding to the problem scene, and the start time and end time corresponding to each of the segmented video files in the temporary cache, obtaining the target segmented video file corresponding to the problem scene from the temporary cache; The target segmented video files corresponding to the problem scene are stored in the local database, and other segmented video files in the temporary cache are deleted; Wherein, obtaining the vehicle driving parameters corresponding to the problem scenario includes: Acquire the local system time corresponding to the problem scenario, and acquire at least one abnormal vehicle simulation model corresponding to the problem scenario and the abnormal vehicle driving parameters corresponding to each abnormal vehicle simulation model from the temporary cache according to the local system time corresponding to the problem scenario and the mapping relationship between the vehicle simulation model, the vehicle driving parameters and the parameter acquisition time; Each of the abnormal vehicle simulation models and the corresponding abnormal vehicle driving parameters are stored in a local database, and the mapping relationship between the vehicle simulation model, the vehicle driving parameters and the parameter acquisition time in the temporary cache is deleted.

2. The method according to claim 1, characterized in that The test vehicle simulation model includes at least one sensor simulation model, and running the traffic flow simulation model in the continuous simulation test scenario includes: The driving environment parameters corresponding to the test vehicle simulation model are obtained through each of the sensor simulation models, and when it is detected that the driving environment parameters corresponding to the test vehicle simulation model meet the preset vehicle alarm conditions, the vehicle alarm information corresponding to the test vehicle simulation model is generated.

3. The method according to claim 1, characterized in that Generating a traffic flow simulation model according to the real traffic flow data includes: Acquire test requirements, and acquire traffic flow setting parameters according to the test requirements and the real traffic flow data; A traffic flow simulation model is generated according to the traffic flow setting parameters.

4. The method according to any one of claims 1 to 3, characterized in that The vehicle driving parameter includes at least one of vehicle speed, vehicle acceleration, vehicle collision time and vehicle distance.

5. The method according to claim 3, characterized in that: The traffic flow setting parameters include at least one of a test vehicle model, a test vehicle driver style, a weather parameter, a number of background vehicles, a background vehicle model, and a background vehicle driver style.

6. A problem scene extraction device for traffic flow simulation, characterized in that: The problem scenario extraction method for realizing traffic flow simulation according to any one of claims 1 to 5 comprises: A traffic flow simulation model generation module, used to obtain real traffic flow data and generate a continuous simulation test scenario and a traffic flow simulation model based on the real traffic flow data; Wherein, the traffic flow simulation model includes at least one vehicle simulation model, and the vehicle simulation model includes a test vehicle simulation model; A traffic flow simulation model running module, used for running the traffic flow simulation model in the continuous simulation test scenario; A vehicle driving parameter acquisition module, used to acquire at least one vehicle driving parameter corresponding to each of the vehicle simulation models during the operation of the traffic flow simulation model, and a parameter acquisition time corresponding to each of the vehicle driving parameters, and generate a mapping relationship between the vehicle simulation model, the vehicle driving parameter and the parameter acquisition time and store it in a temporary cache; A segmented video file acquisition module is used to perform segmented video recording of the operation process of the traffic flow simulation model to obtain at least one segmented video file, and a start time and an end time corresponding to each segmented video file, and store each segmented video file and the corresponding start time and end time in the temporary cache; A problem scenario determination module is used to determine the current test scenario as a problem scenario when the vehicle warning information corresponding to the test vehicle simulation model is detected by a preset vehicle warning detection method, and obtain the vehicle driving parameters corresponding to the problem scenario; A target segmented video file acquisition module, used to acquire the target segmented video file corresponding to the problem scene from the temporary cache according to the local system time corresponding to the problem scene and the start time and end time corresponding to each segmented video file in the temporary cache; A target segmented video file storage module, used to store the target segmented video file corresponding to the problem scene in a local database, and delete other segmented video files in the temporary cache; Wherein, obtaining the vehicle driving parameters corresponding to the problem scenario includes: Acquire the local system time corresponding to the problem scenario, and acquire at least one abnormal vehicle simulation model corresponding to the problem scenario and the abnormal vehicle driving parameters corresponding to each abnormal vehicle simulation model from the temporary cache according to the local system time corresponding to the problem scenario and the mapping relationship between the vehicle simulation model, the vehicle driving parameters and the parameter acquisition time; Each of the abnormal vehicle simulation models and the corresponding abnormal vehicle driving parameters are stored in a local database, and the mapping relationship between the vehicle simulation model, the vehicle driving parameters and the parameter acquisition time in the temporary cache is deleted.

7. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the problem scenario extraction method for traffic flow simulation according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the problem scenario extraction method for traffic flow simulation according to any one of claims 1 to 5 when executed.

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