WO2023226733A1 - Vehicle scene data acquisition method and apparatus, storage medium and electronic device - Google Patents

Abstract

Disclosed in embodiments of the present disclosure are a vehicle scene data acquisition method and apparatus, a storage medium, and an electronic device. The method comprises: in the vehicle running process, obtaining target data determined by an automatic driving system of the vehicle, wherein the target data comprises at least one of the following: a control instruction of the automatic driving system on the vehicle, sensing data sensed by the automatic driving system for the environment where the vehicle is located, positioning data of the vehicle determined by the automatic driving system, and prediction planning data predicted by the automatic driving system; determining whether the vehicle is in a target scene according to the target data; and under the condition that it is determined that the vehicle is in the target scene, collecting vehicle scene data of the vehicle in the target scene.

Description

Vehicle scene data collection methods, devices, storage media and electronic equipment

cross-citation

This disclosure requests the priority of the Chinese patent application filed with the China Patent Office on May 27, 2022, with application number 202210590860. incorporated herein by reference.

Technical field

The present disclosure relates to the field of communications, and specifically, to a vehicle scene data collection method, device, storage medium and electronic device.

Background technique

With the rapid development of science and technology, the field of vehicle automatic driving is developing rapidly, but automatic driving can be divided into different levels. The higher the level of automatic driving, the more scenarios it can adapt to, that is, automatic driving can be achieved without limiting the scenarios, that is, The development of high-level autonomous driving systems should be based on scenarios with no boundary restrictions as verification assumptions, and should have the ability to cover complex environments and unfamiliar and unexpected scenarios.

The autonomous driving algorithm needs to deal with many and complex scenarios. The algorithm has a long life cycle and requires long-term iterative optimization. However, in the current process of testing the autonomous driving system, the test is based on fixed test scenarios, and it is found that the autonomous driving algorithm cannot When covering the current working conditions, testers manually record problems, optimize the software, and then test, which is inefficient. There will also be some edge scenarios where the test cannot be recognized, resulting in the inability to discover all problems with the software. That is, the collection of existing scene data is all artificially simulated scenes, and testers manually trigger the collection of scenes, resulting in less scene data that can be obtained and lack of authenticity.

Regarding related technologies, there is currently no effective solution to the problem of obtaining less vehicle scene data.

Therefore, it is necessary to improve the related technology to overcome the defects in the related technology.

Contents of the invention

In view of this, the purpose of the present disclosure is to provide a vehicle scene data collection method, device, storage medium and electronic device, so as to at least solve the technical problem of less vehicle scene data being obtained.

In order to achieve the above object, in a first aspect, the present disclosure provides a method for collecting vehicle scene data. The method includes: during the driving process of the vehicle, acquiring target data determined by the automatic driving system of the vehicle, wherein: The target data includes at least one of the following: control instructions of the vehicle by the automatic driving system, sensing data of the environment in which the vehicle is located by the automatic driving system, and the vehicle determined by the automatic driving system. The positioning data and the prediction planning data predicted by the automatic driving system; determine whether the vehicle is in the target scene according to the target data; when it is determined that the vehicle is in the target scene, collect the location of the vehicle in the target scene. Vehicle scene data in the target scene.

In an exemplary embodiment, when the target data includes the control instruction, determining whether the vehicle is in a target scene according to the target data includes: determining that the vehicle is in an autonomous driving mode, and When the control instruction indicates that the acceleration of the vehicle in the first direction is set to the first acceleration, it is determined that the vehicle is in an abnormal driving scene, wherein the first direction is the forward direction of the vehicle, so The value of the first acceleration exceeds the first threshold; after determining that the vehicle is in the automatic driving mode, the control instruction instructs to set the acceleration of the vehicle in the second direction to the second acceleration, and instructs to set the vehicle's tires to When the turning angle change rate is set as the target turning angle change rate, it is determined that the vehicle is in an abnormal driving scene, wherein the second acceleration exceeds a second threshold, the target turning angle changing rate exceeds a third threshold, and the second The angle between the direction and the second direction is a preset angle; when it is determined that the vehicle is in manual driving mode, determine the target control instruction issued by the target object to the vehicle; combine the target control instruction with The control instructions in the target data are compared, and when the similarity between the target control instruction and the control instruction is less than a fourth threshold, it is determined that the vehicle is in a predictive control instruction abnormal scenario; wherein, the target Scenarios include: the abnormal driving scene and the abnormal predictive control instruction scene.

In an exemplary embodiment, when the target data includes the perception data, determining whether the vehicle is in a target scene according to the target data includes: determining the automatic driving according to the perception data. When the type of the first obstacle sensed by the sensing module of the system changes within the first preset time period, it is determined that the vehicle is in an abnormal scene of the sensing module; when it is determined based on the sensing data that the identification of the first obstacle is in Occurs within the first preset time period If there is a change, it is determined that the vehicle is in an abnormal scene of the sensing module; if it is determined based on the sensing data that the change in the moving speed of the first obstacle within the first preset time period exceeds the fifth threshold, it is determined that the The vehicle is in an abnormal scene of the sensing module; when it is determined according to the sensing data that the position of the second obstacle sensed by the sensing module changes within a first preset time period, it is determined that the vehicle is in an abnormal scene of the sensing module; When it is determined based on the sensing data that the position of the third obstacle sensed by the sensing module overlaps with the position of the vehicle, it is determined that the vehicle is in an abnormal scene of the sensing module; when the sensing data is the preset sensing data, and when the body data of the vehicle is preset body data, it is determined that the vehicle is in a preset scene; wherein the target scene includes: an abnormal scene of the sensing module and the preset scene.

In an exemplary embodiment, when the target data includes the positioning data, determining whether the vehicle is in a target scene according to the target data includes: determining the location of the vehicle according to the positioning data. When the change in position within the second preset time period exceeds the sixth threshold, it is determined that the vehicle is in an abnormal scene of the positioning module; when it is determined based on the positioning data that the moving direction of the vehicle is within the second preset time period. If the change amount exceeds the seventh threshold, it is determined that the vehicle is in an abnormal scene of the positioning module; it is determined based on the positioning data that the position of the vehicle has not changed within the second preset time period, but the When the speed of the vehicle is the target speed, it is determined that the vehicle is in an abnormal scene of the positioning module; wherein the target scene includes: an abnormal scene of the positioning module.

In an exemplary embodiment, when the target data includes the predictive planning data, determining whether the vehicle is in a target scenario according to the target data includes: when the predictive planning data is used to indicate the first In the case of the predicted movement state of three obstacles, determine the similarity between the predicted movement state and the target movement state of the third obstacle, and when the similarity is less than an eighth threshold, determine the vehicle In an abnormal scenario of the prediction planning module, the target movement state is the movement state obtained after detecting the third obstacle; when the prediction planning data is used to indicate the planned movement trajectory of the vehicle , determine the feasibility of the planned movement trajectory, and when the feasibility is less than the ninth threshold, determine that the vehicle is in an abnormal scenario of the prediction planning module; where the prediction planning data is used to indicate the planning of the vehicle In the case of a movement trajectory, determine whether there are obstacles on the planned movement trajectory, and if it is determined that there are obstacles on the planned movement trajectory, determine that the vehicle is in an abnormal scenario of the prediction planning module; in the prediction planning Data used to indicate that the autonomous driving system is not Under the condition that the vehicle cannot be controlled, it is determined that the vehicle is in a scene to be manually controlled; wherein the target scene includes: the abnormal scene of the prediction planning module and the scene to be manually controlled.

In an exemplary embodiment, collecting vehicle scene data of the vehicle in the target scene includes: collecting body data of the vehicle within a preset time period, wherein within the preset time period, , the vehicle is in the target scene; the chassis data of the vehicle is collected within the preset time period; the process data and control data generated by the automatic driving system of the vehicle during the preset time period are collected Instruction data: Collect the sensing data of the environment in which the vehicle is located, determined by the image acquisition device and the radar sensor within the preset time period.

In an exemplary embodiment, after collecting vehicle scene data of the vehicle in the target scene, the method further includes: sending the vehicle scene data to a cloud server, so that the cloud server can The vehicle scene data adjusts the algorithm of the automatic driving system; obtains the adjusted target algorithm from the cloud server, and updates the algorithm of the automatic driving system to the target algorithm.

In a second aspect, the present disclosure also provides a device for collecting vehicle scene data. The device includes: an acquisition module configured to acquire target data determined by the automatic driving system of the vehicle while the vehicle is driving, wherein, The target data includes at least one of the following: the control instructions of the automatic driving system to the vehicle, the sensing data of the environment in which the vehicle is located, the sensing data determined by the automatic driving system. The positioning data of the vehicle and the prediction planning data predicted by the automatic driving system; a determination module configured to determine whether the vehicle is in the target scene according to the target data; an acquisition module configured to determine whether the vehicle is in the target scene In the case of , collect vehicle scene data of the vehicle in the target scene.

In a third aspect, the present disclosure also provides a computer-readable storage medium. The computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to perform any of the above. A method for collecting vehicle scene data described in a technical solution.

In a fourth aspect, the present disclosure also provides an electronic device, the electronic device includes one or more processors; a storage device configured to store one or more programs, when the one or more programs are processed by the one When executed by or multiple processors, the one or more processors are configured to run a program, wherein the program is configured to execute the vehicle scene data collection method described in any of the above technical solutions when running. .

In the present disclosure, during the driving process of the vehicle, the vehicle automatically determines whether it is within the set target. Scenarios trigger data collection. Since the vehicle's ability to recognize scenes is stronger than that of manual recognition, the vehicle can recognize more scenes, and the scenes in which the vehicle is driving are diverse, so it can collect A large amount of real and diverse scene data solves the problem of less vehicle scene data being obtained.

In order to make the above-mentioned objects, features and advantages of the present disclosure more obvious and understandable, preferred embodiments are given below and described in detail with reference to the accompanying drawings.

Description of the drawings

The drawings described here are used to provide a further understanding of the present disclosure and constitute a part of the present application. The illustrative embodiments of the present disclosure and their descriptions are provided to explain the present disclosure and do not constitute an improper limitation of the present disclosure. In the attached picture:

Figure 1 is a hardware structure block diagram of a computer terminal of a vehicle scene data collection method according to an embodiment of the present disclosure;

Figure 2 is a flow chart of a vehicle scene data collection method according to an embodiment of the present disclosure;

Figure 3 is the hardware architecture of a vehicle-side data collection unit according to an embodiment of the present disclosure;

Figure 4 is a data collection triggering mechanism diagram (1) according to an embodiment of the present disclosure;

Figure 5 is a schematic diagram of a data collection triggering scenario according to an embodiment of the present disclosure;

Figure 6 is a data collection triggering mechanism diagram (2) according to an embodiment of the present disclosure;

FIG. 7 is a structural block diagram of a device for collecting vehicle scene data according to an embodiment of the present disclosure.

Detailed ways

Below, specific embodiments of the present disclosure are described in detail with reference to the accompanying drawings, but are not intended to limit the disclosure.

It will be understood that various modifications may be made to the embodiments disclosed herein. Therefore, the above description should not be regarded as limiting, but as examples of embodiments. Other modifications within the scope and spirit of this disclosure will occur to those skilled in the art.

The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the disclosure and, together with the general description of the disclosure given above and the detailed description of the embodiments given below, are intended to explain the disclosure. principle.

These and other features of the present disclosure will become apparent from the following description of preferred forms of embodiments given as non-limiting examples with reference to the accompanying drawings.

It should also be understood that, although the disclosure has been described with reference to a few specific examples, those skilled in the art will be able to undoubtedly implement many other equivalent forms of the disclosure having the rights to The features described in the claims are therefore within the scope of protection defined thereby.

The above and other aspects, features and advantages of the present disclosure will become more apparent in view of the following detailed description when taken in conjunction with the accompanying drawings.

Specific embodiments of the present disclosure are described hereinafter with reference to the accompanying drawings; however, it is to be understood that the disclosed embodiments are examples of the disclosure, which can be implemented in various ways. Well-known and/or repeated functions and structures have not been described in detail to avoid obscuring the disclosure with unnecessary or redundant detail. Therefore, specific structural and functional details disclosed herein are not intended to be limiting, but rather serve as a basis and representative basis for the claims, and serve as a basis for teaching one skilled in the art to variously employ the present disclosure in substantially any suitable detailed structure. .

It should be noted that the terms "first", "second", etc. in the description and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It is to be understood that the data so used are interchangeable under appropriate circumstances so that the embodiments of the disclosure described herein can be practiced in sequences 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, e.g., a process, method, system, product, or apparatus that encompasses a series of steps or units and need not be limited to those explicitly listed. Those steps or elements may instead include other steps or elements not expressly listed or inherent to the process, method, product or apparatus.

This specification may use the phrases "in one embodiment," "in another embodiment," "in yet another embodiment," or "in further embodiments," which may each refer to the same thing in accordance with the present disclosure. or one or more of the different embodiments.

The present disclosure will be further described below in conjunction with the accompanying drawings and specific embodiments.

The method embodiments provided in the embodiments of this application can be executed in a computer terminal or similar computing device. Taking running on a computer terminal as an example, FIG. 1 is a hardware structure block diagram of a computer terminal for a vehicle scene data collection method according to an embodiment of the present disclosure. As shown in FIG. 1 , the computer terminal may include one or more (one is shown in FIG. 1 ) processors 102 (the processor 102 may include but is not limited to a microprocessor unit (MPU for short) or a programmable logic device. (Programmable logic device, referred to as PLD)) and a memory 104 configured to store data. In an exemplary embodiment, the above-mentioned computer terminal may also include a transmission device 106 configured to have a communication function and an input and output device 108. Persons of ordinary skill in the art can understand that the structure shown in FIG. 1 is schematic and does not limit the structure of the above-mentioned computer terminal. For example, a computer terminal may also include more or fewer components than shown in Figure 1, Or a different configuration with equivalent functions or more functions than those shown in FIG. 1 .

The memory 104 may be configured to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the vehicle scene data collection method in the embodiment of the present disclosure. The processor 102 runs the computer program stored in the memory 104 , thereby executing various functional applications and data processing, that is, implementing the above method. Memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely relative to the processor 102, and these remote memories may be connected to the computer terminal through a network. Examples of the above-mentioned networks include but are not limited to the Internet, intranets, local area networks, mobile communication networks and combinations thereof.

The transmission device 106 is arranged to receive or send data via a network. Specific examples of the above-mentioned network may include a wireless network provided by a communication provider of the computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC for short), which can be connected to other network devices through a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (Radio Frequency, RF for short) module, which is configured to communicate with the Internet wirelessly.

Since self-driving vehicles can collect a large amount of real scene data while driving, in order to efficiently filter out the scenes required by self-driving from a large amount of scene data, this disclosure proposes a vehicle scene data collection method, which can be used in automatic driving and manual driving respectively. Real-scenario data collection is carried out under driving conditions. The autonomous driving system continues to run. In autonomous driving mode, instructions are output to control vehicle driving. In manual driving mode, virtual instructions are output to compare the differences with the actual driver. Through automatic driving abnormal scene recognition, human and vehicle Difference comparison analysis and set scenes of interest effectively trigger and collect real scene data on the vehicle side.

In this embodiment, a vehicle scene data collection method is provided. Figure 2 is a flow chart of a vehicle scene data collection method according to an embodiment of the present disclosure. The process includes the following steps:

Step S202: While the vehicle is driving, obtain the target data determined by the automatic driving system of the vehicle, where the target data includes at least one of the following: the control instructions of the automatic driving system for the vehicle, the Sensing data perceived by the autonomous driving system of the environment in which the vehicle is located, positioning data of the vehicle determined by the autonomous driving system, and predictive planning data predicted by the autonomous driving system;

As an optional example, the above control instructions include but are not limited to: control vehicle acceleration, control vehicle braking and deceleration, control vehicle turning, control vehicle U-turn, control vehicle turn on lights and other control instructions.

As an optional example, the above-mentioned sensing data includes but is not limited to: sensing the number, type, identification, speed, location, driving status, etc. of obstacles around the vehicle.

As an optional example, the above positioning data includes but is not limited to: the position of the vehicle, the moving direction (heading) of the vehicle, etc.

As an optional example, the above-mentioned predictive planning data includes but is not limited to: the predicted movement state of the obstacle (for example, the obstacle moves in the x direction at x speed), the planned travel trajectory of the vehicle, and so on.

Step S204: Determine whether the vehicle is in a target scene according to the target data;

It should be noted that the target scenario is the scenario where the tester wants to collect vehicle scene data. The target scenario includes but is not limited to: abnormal driving scenarios, abnormal predictive control instruction scenarios, abnormal sensing module scenarios, preset scenarios customized by the tester, Abnormal scenarios of the positioning module, abnormal scenarios of the prediction and planning module, and scenarios to be manually controlled. These scenarios will be described in detail below and will not be described in detail here.

Step S206: When it is determined that the vehicle is in the target scene, collect vehicle scene data of the vehicle in the target scene.

As an optional example, collecting vehicle scene data of the vehicle in the target scene can be achieved in the following ways:

Collecting body data of the vehicle within a preset time period, wherein the vehicle is in the target scene during the preset time period; collecting chassis data of the vehicle within the preset time period; Collect the process data and control instruction data generated by the automatic driving system of the vehicle within the preset time period; collect the image acquisition device and radar sensor determined by the vehicle within the preset time period. Perception data of the vehicle’s environment.

It should be noted that vehicle scene data includes: body data, chassis data, process data generated by the autonomous driving system, control instruction data, and perception data.

Specifically, Figure 3 is the hardware architecture of a vehicle-side data collection unit according to an embodiment of the present disclosure. Vehicle scene data can be collected through the vehicle-side data collection unit as shown in Figure 3, where the vehicle-side data collection unit includes: Data acquisition triggering calculation unit, data storage unit;

The input of the vehicle-side data collection unit includes:

(1) Vehicle body data, chassis data and other information transmitted through the gateway;

(2) Process data and control instruction data generated by the automatic driving control unit of the automatic driving system;

(3) Sensing data perceived by the vehicle through image acquisition devices (such as cameras) and radar sensors (such as lidar).

As an optional example, after performing the above step S206, the vehicle scene data also needs to be sent to the cloud server, so that the cloud server adjusts the algorithm of the automatic driving system according to the vehicle scene data; obtain The cloud server adjusts the target algorithm, and updates the algorithm of the autonomous driving system to the target algorithm.

Optionally, the vehicle scene data stored in the data storage unit in the vehicle-side data acquisition unit can be uploaded to the cloud server through the communication module as shown in Figure 3.

Through the above steps, while the vehicle is driving, the vehicle automatically determines whether it is in the set target scene, and then triggers data collection. Since the vehicle's ability to identify scenes is stronger than that of manual identification, the vehicle can then identify There are more scenes, and the scenes in which the vehicle is driving are diverse, so a large amount of real and diverse scene data can be collected, thus solving the problem of less vehicle scene data being obtained.

As an optional example, when the target data includes the control instruction, determining whether the vehicle is in the target scene according to the target data is implemented by the following methods one to three:

Method 1: When it is determined that the vehicle is in the automatic driving mode and the control instruction indicates that the acceleration of the vehicle in the first direction is set to the first acceleration, it is determined that the vehicle is in an abnormal driving scenario, wherein, The first direction is the forward direction of the vehicle, the value of the first acceleration exceeds a first threshold, and the target scene includes: an abnormal driving scene;

It should be noted that if the control instruction indicates that the acceleration of the vehicle in the first direction is set to the first acceleration, it means that the automatic driving system needs to control the vehicle to brake suddenly, that is, the current vehicle is in a sudden extreme state, that is, it is in an abnormal driving scenario. Down.

Method 2: After it is determined that the vehicle is in the autonomous driving mode, the control instruction instructs to set the acceleration of the vehicle in the second direction as the second acceleration, and instructs to set the rotation angle change rate of the vehicle's tires as the target rotation angle. In the case of change rate, it is determined that the vehicle is in an abnormal driving scene, wherein the second acceleration exceeds a second threshold, the target angle change rate exceeds a third threshold, and the second direction is The angle between is the preset angle;

It should be noted that if the control instruction indicates that the acceleration of the vehicle in the second direction is set to the second acceleration and the rotation angle change rate of the vehicle's tires is set to the target angle change rate, it means that the automatic driving needs to control the vehicle to turn sharply. Then the current vehicle is in a sudden extreme state, that is, in an abnormal driving scenario.

Method 3: When it is determined that the vehicle is in manual driving mode, determine the target control instruction issued by the target object to the vehicle; compare the target control instruction with the control instruction in the target data, and compare the target control instruction with the control instruction in the target data. If the similarity between the target control instruction and the control instruction is less than the fourth threshold, it is determined that the vehicle is in a predictive control instruction abnormal scene, where the target scene includes: a predictive control instruction abnormal scene.

It should be noted that in manual driving mode, the automatic driving system simulates operation, and the control instructions issued by it do not directly control the vehicle. At this time, the driver's operating behavior and the actual state of the vehicle are different from the control instructions of the automatic driving system. After analysis, when the difference between the two exceeds the set fourth threshold, it means that the vehicle is determined to be in an abnormal scenario of predictive control instructions.

Optionally, if the target control instruction is to control the vehicle to turn left, and the control instruction is to control the vehicle to turn right, it means that the similarity between the two is less than the fourth threshold.

As an optional example, when the target data includes the sensing data, determining whether the vehicle is in the target scene based on the target data can be determined through the following methods four to nine:

Method 4: When it is determined based on the sensing data that the type of the first obstacle sensed by the sensing module of the automatic driving system changes within the first preset time period, it is determined that the vehicle is in an abnormal scene of the sensing module, Among them, the target scenarios include: abnormal sensing module scenarios;

Optionally, the first preset time period may be 1 second, 2 seconds, etc., and the first obstacle is a movable obstacle (for example: movable pedestrians or vehicles on the road). If the type of the first obstacle is in the first If a change occurs within a preset period of time (for example, there is a pedestrian at the beginning and a car behind), it means that the type of obstacle perceived changes, that is, the sensing module is abnormal and the vehicle is in an abnormal scene of the sensing module.

Method 5: When it is determined based on the sensing data that the identification of the first obstacle changes within the first preset time period, it is determined that the vehicle is in an abnormal scene of the sensing module;

It should be noted that if the identity of the first obstacle changes within the first preset time period (for example: initially the identity of the first obstacle is vehicle 1, and then changes to vehicle 2), it means that the perceived obstacle The object's identification jumps, that is, the sensing module is abnormal, and the vehicle is in an abnormal scene of the sensing module.

Method 6: When it is determined based on the sensing data that the change in the moving speed of the first obstacle within the first preset time period exceeds the fifth threshold, it is determined that the vehicle is in an abnormal scene of the sensing module;

It should be noted that if the change in the moving speed of the first obstacle exceeds the fifth threshold within the first preset time period, it means that the speed of the perceived obstacle has jumped, that is, the sensing module is abnormal and the vehicle is in the sensing module. Unusual scene.

Method 7: When it is determined based on the sensing data that the position of the second obstacle sensed by the sensing module changes within the first preset time period, it is determined that the vehicle is in an abnormal scene of the sensing module;

It should be noted that the second obstacle is a stationary obstacle (for example, a tree, a house, etc.). If the position of the second obstacle changes within the first preset time period, it means that the position of the stationary obstacle jumps, that is, the perception The module is abnormal and the vehicle is in a scenario where the sensing module is abnormal.

Method 8: When it is determined based on the sensing data that the position of the third obstacle sensed by the sensing module overlaps with the position of the vehicle, it is determined that the vehicle is in an abnormal scene of the sensing module;

It should be noted that the third obstacle includes the first obstacle and the second obstacle.

Method 9: When the sensing data is preset sensing data and the vehicle body data is preset body data, determine that the vehicle is in a preset scene, where the target scene includes: a preset scene.

Optionally, the autonomous driving system will be developed for specific scenarios, so data collection for characteristic scenarios is required. Specifically, the preset scenes of interest can be configured on the cloud server, and the scenes that need to be collected are sent to the vehicle according to specific needs. When the vehicle receives the preset scene requirements, it will identify the scene and trigger data recording. , upload to the cloud server. For example, if it is necessary to collect autonomous driving lane changing scene data, it can be sent to the car. The car will then identify the vehicle's lane changing behavior based on the sensing data, then record the overall lane changing process data and upload it to the cloud storage application.

Preset scenes can be comprehensively judged based on perception, positioning and high-precision map information, such as: (1) road type (highway/city/park...), shape (straight/curve/U-turn...), number of lanes, traffic lights; (2) ) The number, category, location, speed, etc. of obstacles; (3) The current speed, acceleration, lighting status, etc. of the vehicle. Among them, the preset sensing data includes but is not limited to (1) above, the preset body Data includes but is not limited to (3) above.

As an optional example, when the target data includes the positioning data, determining whether the vehicle is in the target scene based on the target data can be determined through the following methods ten to twelve:

Method 10: When it is determined based on the positioning data that the change in the position of the vehicle within the second preset time period exceeds the sixth threshold, it is determined that the vehicle is in an abnormal scene of the positioning module, wherein the target scene includes : Abnormal scenario of the positioning module;

Optionally, the second preset duration may be 1 second or 2 seconds.

It should be noted that if the change in the vehicle's position within the second preset time period exceeds the sixth threshold (for example, from positioning Beijing to positioning Wuhan), it means that the position of the vehicle itself jumps, that is, the positioning of the autonomous driving system. The module is abnormal and the vehicle is in a positioning module abnormality scenario.

Method 11: When it is determined based on the positioning data that the change in the movement direction of the vehicle within the second preset time period exceeds a seventh threshold, it is determined that the vehicle is in an abnormal scene of the positioning module;

It should be noted that if the change in the vehicle's moving direction within the second preset time period exceeds the seventh threshold (for example, a sudden change from forward driving to backward driving), it means that the vehicle's own heading jumps. , that is, the positioning module of the automatic driving system is abnormal, and the vehicle is in a positioning module abnormality scenario.

Method 12: When it is determined based on the positioning data that the position of the vehicle has not changed within the second preset time period, but the speed of the vehicle is the target speed, it is determined that the vehicle is in a positioning module abnormality. Scenes;

It should be noted that if the position of the vehicle does not change but the vehicle has speed, it means that the positioning module of the automatic driving system is abnormal and the vehicle is in a positioning module abnormality scenario.

As an optional example, when the target data includes the prediction data, determining whether the vehicle is in the target scene based on the target data can be determined through the following methods thirteen to sixteen:

Method 13: In the case where the predicted data is used to indicate the predicted movement state of the third obstacle, determine the similarity between the predicted movement state and the target movement state of the third obstacle, and determine the similarity between the predicted movement state and the target movement state of the third obstacle. If the degree is less than the eighth threshold, it is determined that the vehicle is in an abnormal scenario of the predictive planning module, wherein the target movement state is the movement state obtained after detecting the third obstacle, and the target scenario includes: predictive planning Module abnormality scenarios;

It should be noted that if the predicted movement state of the third obstacle predicted by the autonomous driving module is significantly different from the actual movement state of the detected third obstacle, it means that the predictive planning module of the autonomous driving system is abnormal, that is, the vehicle is in the predicted state. Planning module abnormal scenarios.

Method 14: When the predicted planning data is used to indicate the planned movement trajectory of the vehicle, determine the feasibility of the planned movement trajectory, and when the feasibility is less than the ninth threshold, determine the feasibility of the planned movement trajectory. The vehicle described above is in an abnormal scenario in the predictive planning module;

Method 15: When the predicted planning data is used to indicate the planned movement trajectory of the vehicle, determine whether there are obstacles on the planned movement trajectory, and determine whether there are obstacles on the planned movement trajectory. Next, it is determined that the vehicle is in an abnormal scenario of the prediction planning module;

Method 16: When the predictive planning data is used to indicate that the automatic driving system cannot control the vehicle, determine that the vehicle is in a scenario to be manually controlled, where the target scenario includes a scenario to be manually controlled.

Obviously, the above-described embodiments are part of the embodiments of the present disclosure, rather than all embodiments. In order to better understand the above-mentioned vehicle scene data collection method, the above-mentioned process will be described below with reference to embodiments, but this is not intended to limit the technical solutions of the embodiments of the present disclosure, specifically:

In order to realize automatic driving vehicles to fully and effectively collect real scene data, this disclosure proposes a vehicle scene data collection method, which can trigger data collection in both automatic driving and manual driving modes. It is specifically divided into automatic driving anomaly identification, human driving Vehicle difference analysis and identification and interest scene identification, and then upload the data to the cloud server for storage and application.

When the vehicle is in the autonomous driving mode, Figure 4 is a diagram (1) of the data collection trigger mechanism according to an embodiment of the present disclosure. As shown in Figure 4, the data collection trigger mechanism is divided into two situations.

(1) Automatic driving abnormal scene recognition

Abnormal scene recognition of autonomous driving mainly includes five situations. Figure 5 is a schematic diagram of a data collection triggering scene according to an embodiment of the present disclosure;

The specific instructions are as follows:

(11) Automatic driving abnormal takeover (equivalent to the scene to be manually controlled in the above embodiment)

When autonomous driving is running, if a scene cannot be controlled by the system, the driver will be prompted to take over manually. Data recording for such scenes can be triggered based on the takeover signal.

(12) Automatic driving control command abnormality (equivalent to the abnormal driving scene in the above embodiment)

When sudden braking occurs during autonomous driving operation, it means that the vehicle is currently in an abnormal or extreme scene and needs to record data for further analysis. Data recording can be triggered based on the longitudinal (equivalent to the first direction in the above embodiment) acceleration exceeding a certain threshold.

When a sharp turn occurs during autonomous driving operation, it indicates that the current situation is an abnormal or extreme scene, and the data needs to be recorded for further analysis. According to the lateral (equivalent to the second direction in the above embodiment) acceleration exceeds a certain threshold, and the change rate of the turning angle exceeds A certain threshold is used to trigger data recording.

(13) Sensing data abnormality (equivalent to the sensing module abnormality scenario in the above embodiment)

Comprehensive judgment is made on the historical sensing data for a period of time, and data recording is triggered when it is found that the type of perceived obstacle changes, speed jumps, obstacle ID jumps, stationary obstacle position jumps, etc.

Data recording is triggered when common sense errors such as overlapping of the obstacle recognition position with the own vehicle occur.

(14) Positioning data abnormality (equivalent to the positioning module abnormality scenario in the above embodiment)

Comprehensive judgment is made on the historical positioning data for a period of time, and data recording is triggered when it is found that the vehicle's position jumps, the vehicle's heading jumps, the vehicle speed is constant but the vehicle's position remains unchanged, etc.

(15) Forecast planning data anomaly (equivalent to the planning prediction module abnormality scenario in the above embodiment)

Comprehensive judgment is made on historical prediction and planning data for a period of time. When it is found that there is a large difference between the obstacle prediction and the actual obstacle true value, a feasible trajectory cannot be planned, or the planned trajectory collides with obstacles, data recording is triggered.

It should be noted that autonomous driving systems generally include perception, positioning, and predictive planning modules. When functional modules in the autonomous driving system are abnormal, data recording is triggered.

(2) Identification of interesting scenes (equivalent to the preset scenes in the above embodiment)

Autonomous driving will be developed for specific scenarios, so data collection for characteristic scenarios is required. As shown in Figure 4, the scene of interest is configured in the cloud, and the scene to be collected is sent to the vehicle according to specific needs. When the vehicle receives the demand for the scene of interest, it identifies the scene, triggers data recording, and uploads it to cloud. For example, if it is necessary to collect autonomous driving lane changing scene data, it can be sent to the car. The car will then identify the vehicle's lane changing behavior based on the sensing data, then record the overall lane changing process data and upload it to the cloud storage application.

It should be noted that the scene of interest can be comprehensively judged based on perception, positioning and high-precision map information:

(21) Road type (highway/city/park...), shape (straight/curve/U-turn...), number of lanes, and traffic lights;

(22) Number, type, location, speed, etc. of obstacles;

(23) Current speed, acceleration, lighting status of the vehicle, etc.

When the vehicle is in the manual driving mode, Figure 6 is a diagram (2) of the data collection triggering mechanism according to an embodiment of the present disclosure. As shown in Figure 3, the data collection triggering mechanism is divided into three situations:

(1) Analysis and identification of differences between people and vehicles

In manual driving mode, the automatic driving system simulates operation, and its control instructions do not directly control the vehicle. At this time, the driver's operating behavior and the actual state of the vehicle are analyzed for differences with the control instructions of the automatic driving system. When the difference between the two exceeds the set When a set threshold is reached (when the difference between the two exceeds the set threshold, it indicates that the vehicle is in an abnormal scenario of predictive control instructions), data recording is triggered.

(2) Automatic driving anomaly identification

In manual driving mode, automatic driving anomaly identification includes sensing data anomalies, positioning data anomalies and planning data anomalies. The triggering methods of these three types of anomalies are consistent with those in automatic driving mode.

(3) Interesting scene recognition

The identification of interesting scenes in manual driving mode is consistent with that in automatic driving mode.

It should be noted that in order to achieve effective collection of vehicle-side data of the automatic driving system, this disclosure designs a trigger mechanism for data collection and storage in both automatic driving mode and manual driving mode.

In the automatic driving mode, the automatic driving abnormal scene data trigger and the interesting scene trigger are designed to improve the effective collection of data in the automatic driving mode. In manual driving mode, through the virtual operation of the automatic driving system, based on the collection of abnormal scene data of automatic driving and scenes of interest, the difference scene trigger between humans and vehicles can be added to quickly collect the gap between the automatic driving system and the manual driver. Effectively improve the coverage of vehicle-side scenarios and provide scenario data support for the optimization and development of autonomous driving systems.

Among them, in order to effectively collect abnormal scenario data of autonomous driving, data collection is triggered in five situations, including abnormal takeover scenarios of autonomous driving, abnormal autonomous driving control instructions, and perception Data anomalies, positioning data anomalies and predictive planning data anomalies can effectively trigger abnormal scenarios from the overall and sub-module levels.

In order to realize data collection of specific scenes, a scene identification module of interest is designed, and a cloud scene configuration function is designed, which can remotely deliver scenes of interest in the cloud and quickly realize data collection of specific scenes.

Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by means of software plus the necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is Better implementation. Based on this understanding, the technical solution of the present disclosure can be embodied in the form of a software product in essence or that contributes to the existing technology. The computer software product is stored in a storage medium (such as ROM/RAM, disk, CD), including several instructions to cause a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of various embodiments of the present disclosure.

This embodiment also provides a device for collecting vehicle scene data. The device is configured to implement the above embodiments and preferred implementations. What has already been explained will not be described again. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.

Figure 7 is a structural block diagram of a device for collecting vehicle scene data according to an embodiment of the present disclosure. The device includes:

The acquisition module 72 is configured to acquire target data determined by the automatic driving system of the vehicle while the vehicle is driving, wherein the target data includes at least one of the following: control instructions for the vehicle by the automatic driving system. , the sensing data perceived by the automatic driving system of the environment in which the vehicle is located, the positioning data of the vehicle determined by the automatic driving system, and the predictive planning data predicted by the automatic driving system;

Determining module 74, configured to determine whether the vehicle is in a target scene according to the target data;

The collection module 76 is configured to collect vehicle scene data of the vehicle in the target scene when it is determined that the vehicle is in the target scene.

Through the above device, when the vehicle is driving, the vehicle automatically determines whether the setting In the target scene, data collection is triggered. Since the vehicle's ability to recognize scenes is stronger than that of manual recognition, the vehicle can recognize more scenes, and the scenes in which the vehicle is driving are diverse, so it can A large amount of real and diverse scene data is collected, thus solving the problem of less vehicle scene data being obtained.

In an exemplary embodiment, the determining module is configured to determine whether the vehicle is in a target scene in the following manner: when determining that the vehicle is in an autonomous driving mode, And when the control instruction indicates that the acceleration of the vehicle in the first direction is set to the first acceleration, it is determined that the vehicle is in an abnormal driving scene, wherein the first direction is the forward direction of the vehicle, The value of the first acceleration exceeds a first threshold; or when it is determined that the vehicle is in the automatic driving mode, the control instruction instructs to set the acceleration of the vehicle in the second direction to the second acceleration and instructs the vehicle to When the tire's rotation angle change rate is set to the target rotation angle change rate, it is determined that the vehicle is in an abnormal driving scene, wherein the second acceleration exceeds a second threshold, the target rotation angle change rate exceeds a third threshold, and the The angle between the second direction and the second direction is a preset angle; when it is determined that the vehicle is in manual driving mode, determine the target control instruction issued by the target object to the vehicle; control the target The instruction is compared with the control instruction in the target data, and when the similarity between the target control instruction and the control instruction is less than a fourth threshold, it is determined that the vehicle is in a predictive control instruction abnormal scenario; wherein, The target scenario includes: the driving abnormality scenario and the predictive control instruction abnormality scenario.

In an exemplary embodiment, the determining module is configured to determine whether the vehicle is in the target scene in the following manner: when the target data includes the sensing data, the automatic When the type of the first obstacle sensed by the sensing module of the driving system changes within the first preset time period, it is determined that the vehicle is in an abnormal scene of the sensing module; or when the first obstacle is determined based on the sensing data When the identification changes within the first preset time period, it is determined that the vehicle is in an abnormal scene of the sensing module; or when it is determined based on the sensing data that the change in the moving speed of the first obstacle within the first preset time period exceeds In the case of the fifth threshold, it is determined that the vehicle is in an abnormal scene of the sensing module; or it is determined based on the sensing data that the position of the second obstacle sensed by the sensing module changes within the first preset time period. , it is determined that the vehicle is in a sensing module abnormality scene; when it is determined according to the sensing data that the position of the third obstacle sensed by the sensing module overlaps with the position of the vehicle, it is determined that the vehicle is in a sensing module abnormality scene. scene; described When the sensing data is preset sensing data and the vehicle body data is preset body data, it is determined that the vehicle is in a preset scene; wherein the target scene includes: an abnormal scene of the sensing module, and the Preset scenes.

In an exemplary embodiment, the determination module is configured to determine whether the vehicle is in the target scene in the following manner: when the target data includes the positioning data, the vehicle is determined according to the positioning data. If the change in the position of the vehicle exceeds the sixth threshold within the second preset time period, it is determined that the vehicle is in an abnormal scene of the positioning module; or it is determined based on the positioning data that the moving direction of the vehicle is in the second preset time period. If the amount of change within the duration exceeds the seventh threshold, it is determined that the vehicle is in an abnormal scene of the positioning module; or it is determined based on the positioning data that the position of the vehicle has not changed within the second preset duration, However, when the speed of the vehicle is the target speed, it is determined that the vehicle is in an abnormal scene of the positioning module; wherein the target scene includes: an abnormal scene of the positioning module.

In an exemplary embodiment, the determination module is configured to determine whether the vehicle is in a target scenario in the following manner: when the target data includes the predictive planning data, the predictive planning data is set to indicate In the case of the predicted movement state of the third obstacle, determine the similarity between the predicted movement state and the target movement state of the third obstacle, and when the similarity is less than an eighth threshold, determine the The vehicle is in an abnormal scenario of the prediction planning module, wherein the target movement state is the movement state obtained after detecting the third obstacle; or the prediction planning data is set to indicate the planned movement trajectory of the vehicle. In this case, determine the feasibility of the planned movement trajectory, and when the feasibility is less than the ninth threshold, determine that the vehicle is in an abnormal scenario of the predictive planning module; or when the predictive planning data is set to indicate the In the case of the planned movement trajectory of the vehicle, determine whether there are obstacles on the planned movement trajectory, and if it is determined that there are obstacles on the planned movement trajectory, determine that the vehicle is in an abnormal scenario of the prediction planning module; or When the predictive planning data is set to indicate that the automatic driving system cannot control the vehicle, it is determined that the vehicle is in a scenario to be manually controlled; wherein the target scenario includes: an abnormal scenario of the predictive planning module, and the The scene is to be manually controlled.

In an exemplary embodiment, the determination module is configured to collect vehicle scene data of the vehicle in the target scene in the following manner: collect vehicle body data of the vehicle within a preset time period, wherein, in the During the preset time period, the vehicle is in the target scene; the chassis data of the vehicle during the preset time period is collected; and the chassis data of the vehicle during the preset time period is collected. During the interval, the process data and control instruction data generated by the automatic driving system are collected; the perception data of the environment of the vehicle determined by the image acquisition device and the radar sensor within the preset time period are collected.

In an exemplary embodiment, the above device further includes: a processing module configured to, after collecting vehicle scene data of the vehicle in the target scene, send the vehicle scene data to the cloud server so that the The cloud server adjusts the algorithm of the automatic driving system according to the vehicle scene data; obtains the adjusted target algorithm from the cloud server, and updates the algorithm of the automatic driving system to the target algorithm.

Embodiments of the present disclosure also provide a computer-readable storage medium that stores a computer program, wherein the computer program is configured to execute the steps in any of the above method embodiments when running.

Optionally, in this embodiment, the above-mentioned storage medium may be configured to store a computer program configured to perform the following steps:

S1. During the driving process of the vehicle, obtain the target data determined by the automatic driving system of the vehicle, wherein the target data includes at least one of the following: the control instruction of the automatic driving system to the vehicle, the automatic driving system Sensing data perceived by the driving system of the environment in which the vehicle is located, positioning data of the vehicle determined by the automatic driving system, and predictive planning data predicted by the automatic driving system;

S2, determine whether the vehicle is in the target scene according to the target data;

S3: When it is determined that the vehicle is in the target scene, collect vehicle scene data of the vehicle in the target scene.

In an exemplary embodiment, the computer-readable storage medium may include but is not limited to: U disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM) , mobile hard disk, magnetic disk or optical disk and other media that can store computer programs.

For specific examples in this embodiment, reference may be made to the examples described in the above-mentioned embodiments and exemplary implementations, and details will not be described again in this embodiment.

It should be noted that the above-mentioned storage medium of the present disclosure may be a computer-readable signal medium or or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any combination thereof. More specific examples of computer readable storage media may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard drive, random access memory (RAM), read only memory (ROM), removable Programmed read-only memory (EPROM or flash memory), fiber optics, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above. In this disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code therein. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any storage medium other than computer-readable storage media that can transmit, propagate, or transport a program configured for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the storage medium can be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

Embodiments of the present disclosure also provide an electronic device, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

Optionally, in this embodiment, the above-mentioned processor may be configured to perform the following steps through a computer program:

S1. During the driving process of the vehicle, obtain the target data determined by the automatic driving system of the vehicle, wherein the target data includes at least one of the following: the control instruction of the automatic driving system to the vehicle, the automatic driving system Sensing data perceived by the driving system of the environment in which the vehicle is located, positioning data of the vehicle determined by the automatic driving system, and predictive planning data predicted by the automatic driving system;

S2, determine whether the vehicle is in the target scene according to the target data;

S3: When it is determined that the vehicle is in the target scene, collect the location of the vehicle Vehicle scene data in the target scene.

In an exemplary embodiment, the above-mentioned electronic device may further include a transmission device and an input-output device, wherein the transmission device is connected to the above-mentioned processor, and the input-output device is connected to the above-mentioned processor.

For specific examples in this embodiment, reference may be made to the examples described in the above-mentioned embodiments and exemplary implementations, and details will not be described again in this embodiment.

Obviously, those skilled in the art should understand that the above-mentioned modules or steps of the present disclosure can be implemented using general-purpose computing devices, and they can be concentrated on a single computing device, or distributed across a network composed of multiple computing devices. They may be implemented in program code executable by a computing device, such that they may be stored in a storage device for execution by the computing device, and in some cases may be executed in a sequence different from that shown herein. Or the described steps can be implemented by making them into individual integrated circuit modules respectively, or by making multiple modules or steps among them into a single integrated circuit module. As such, the present disclosure is not limited to any specific combination of hardware and software.

The above are only preferred embodiments of the present disclosure and are not intended to limit the present disclosure. For those skilled in the art, the present disclosure may have various modifications and changes. Any modifications, equivalent substitutions, improvements, etc. made within the principles of this disclosure shall be included in the protection scope of this disclosure.

Industrial applicability

The vehicle scene data collection method and device provided by the embodiments of the present disclosure can be applied to the process of driving the vehicle, and the vehicle automatically determines whether it is in the set target scene, thereby triggering data collection. Since the vehicle's ability to recognize scenes is better than manual The ability to recognize scenes is stronger, and the vehicle can recognize more scenes, and the scenes in which the vehicle is driving are diverse, and a large amount of real and diverse scene data can be collected, thus solving the problem of obtaining vehicle The problem of less scene data.

Claims (10)

1. A method for collecting vehicle scene data, the method includes:

   During the driving process of the vehicle, target data determined by the automatic driving system of the vehicle is obtained, wherein the target data includes at least one of the following: the control instructions of the automatic driving system for the vehicle, the automatic driving system Perception data of the environment in which the vehicle is located, positioning data of the vehicle determined by the autonomous driving system, and predictive planning data predicted by the autonomous driving system;

   Determine whether the vehicle is in a target scene according to the target data;

   When it is determined that the vehicle is in the target scene, vehicle scene data of the vehicle in the target scene is collected.
2. The vehicle scene data collection method according to claim 1, wherein, when the target data includes the control instruction, determining whether the vehicle is in the target scene according to the target data includes:

   When it is determined that the vehicle is in the autonomous driving mode and the control instruction indicates that the acceleration of the vehicle in the first direction is set to a first acceleration, it is determined that the vehicle is in an abnormal driving scenario, wherein the third One direction is the forward direction of the vehicle, and the value of the first acceleration exceeds a first threshold;

   After it is determined that the vehicle is in the autonomous driving mode, the control instruction instructs to set the acceleration of the vehicle in the second direction to the second acceleration, and instructs to set the rotation angle change rate of the vehicle's tires to the target rotation angle change rate. In this case, it is determined that the vehicle is in an abnormal driving scenario, wherein the second acceleration exceeds a second threshold, the target angle change rate exceeds a third threshold, and the angle between the second direction and the second direction is Angle is the preset included angle;

   When it is determined that the vehicle is in manual driving mode, the target control instruction issued by the target object to the vehicle is determined; the target control instruction is compared with the control instruction in the target data, and the target control instruction is If the similarity between the instruction and the control instruction is less than the fourth threshold, it is determined that the vehicle is in a predictive control instruction abnormal scenario;

   Wherein, the target scene includes: the driving abnormality scene and the predictive control instruction abnormality scene.
3. The vehicle scene data collection method according to claim 1, wherein when the target data includes the sensing data, it is determined based on the target data whether the vehicle Be in the target scenario, including:

   If it is determined based on the sensing data that the type of the first obstacle sensed by the sensing module of the autonomous driving system changes within the first preset time period, it is determined that the vehicle is in an abnormal scene of the sensing module;

   In the case where it is determined based on the sensing data that the identification of the first obstacle changes within the first preset time period, it is determined that the vehicle is in a sensing module abnormal scene;

   If it is determined based on the sensing data that the change in the moving speed of the first obstacle within the first preset time period exceeds the fifth threshold, it is determined that the vehicle is in a sensing module abnormal scene;

   If it is determined based on the sensing data that the position of the second obstacle sensed by the sensing module changes within the first preset time period, it is determined that the vehicle is in an abnormal scene of the sensing module;

   When it is determined based on the sensing data that the position of the third obstacle sensed by the sensing module overlaps with the position of the vehicle, it is determined that the vehicle is in an abnormal scene of the sensing module;

   In the case where the sensing data is preset sensing data and the vehicle body data is preset body data, it is determined that the vehicle is in a preset scene;

   Wherein, the target scene includes: the abnormal scene of the sensing module and the preset scene.
4. The vehicle scene data collection method according to claim 1, wherein, when the target data includes the positioning data, determining whether the vehicle is in the target scene according to the target data includes:

   In the case where it is determined based on the positioning data that the change in the position of the vehicle within the second preset time period exceeds a sixth threshold, it is determined that the vehicle is in an abnormal scene of the positioning module;

   When it is determined based on the positioning data that the change in the movement direction of the vehicle within the second preset time period exceeds a seventh threshold, it is determined that the vehicle is in an abnormal scene of the positioning module;

   When it is determined based on the positioning data that the position of the vehicle has not changed within the second preset time period but the speed of the vehicle is the target speed, it is determined that the vehicle is in an abnormal scene of the positioning module;

   Wherein, the target scenario includes: the abnormal scenario of the positioning module.
5. The vehicle scene data collection method according to claim 1, wherein, when the target data includes the prediction planning data, determining whether the vehicle is in the target scene according to the target data includes:

   In the case where the predictive planning data is used to indicate the predicted movement state of the third obstacle, Determine the similarity between the predicted movement state and the target movement state of the third obstacle, and when the similarity is less than an eighth threshold, determine that the vehicle is in a prediction planning module abnormal scenario, wherein, The target movement state is the movement state obtained after detecting the third obstacle;

   When the predicted planning data is used to indicate the planned movement trajectory of the vehicle, the feasibility of the planned movement trajectory is determined, and when the feasibility is less than a ninth threshold, it is determined that the vehicle is in the predicted movement trajectory. Planning module abnormal scenarios;

   In the case where the predicted planning data is used to indicate the planned movement trajectory of the vehicle, determine whether there are obstacles on the planned movement trajectory, and if it is determined that there are obstacles on the planned movement trajectory, determine whether there are obstacles on the planned movement trajectory. The vehicle described above is in an abnormal scenario in the predictive planning module;

   In the case where the predictive planning data is used to indicate that the automatic driving system is unable to control the vehicle, it is determined that the vehicle is in a scenario to be manually controlled;

   Wherein, the target scenario includes: the abnormal scenario of the prediction planning module and the scenario to be manually controlled.
6. The vehicle scene data collection method according to claim 1, wherein collecting the vehicle scene data of the vehicle in the target scene includes:

   Collect body data of the vehicle within a preset time period, wherein the vehicle is in the target scene during the preset time period;

   Collect chassis data of the vehicle within the preset time period;

   Collect the process data and control instruction data generated by the automatic driving system of the vehicle within the preset time period;

   Collect the sensing data of the environment in which the vehicle is located, determined by the image acquisition device and the radar sensor within the preset time period.
7. The vehicle scene data collection method according to claim 1, wherein after collecting the vehicle scene data of the vehicle in the target scene, the method further includes:

   Send the vehicle scene data to a cloud server so that the cloud server adjusts the algorithm of the automatic driving system according to the vehicle scene data;

   Obtain the adjusted target algorithm from the cloud server, and update the algorithm of the automatic driving system to the target algorithm.
8. A device for collecting vehicle scene data, which includes:

   The acquisition module is configured to acquire the automatic driving system of the vehicle while the vehicle is driving. The target data determined by the system, wherein the target data includes at least one of the following: the control instructions of the automatic driving system to the vehicle, the sensing data of the environment in which the vehicle is located, the sensing data of the automatic driving system, the The positioning data of the vehicle determined by the automatic driving system and the predictive planning data predicted by the automatic driving system;

   a determination module configured to determine whether the vehicle is in a target scene according to the target data;

   The collection module is configured to collect vehicle scene data of the vehicle in the target scene when it is determined that the vehicle is in the target scene.
9. A computer-readable storage medium, wherein the computer-readable storage medium includes a stored program, wherein when the program is run, the device where the computer-readable storage medium is located is controlled to execute any one of claims 1 to 7 The vehicle scene data collection method described.
10. An electronic device, wherein the electronic device includes one or more processors; a storage device configured to store one or more programs, and when the one or more programs are executed by the one or more processors, The one or more processors are configured to run a program, wherein the program is configured to execute the vehicle scene data collection method described in any one of claims 1 to 7 when running.