CN115019267A

CN115019267A - Vehicle scene data acquisition method and device, storage medium and electronic equipment

Info

Publication number: CN115019267A
Application number: CN202210590860.XA
Authority: CN
Inventors: 陈志新; 陈博; 尚秉旭; 刘洋; 王洪峰; 张勇
Original assignee: FAW Group Corp
Current assignee: FAW Group Corp
Priority date: 2022-05-27
Filing date: 2022-05-27
Publication date: 2022-09-06
Also published as: WO2023226733A1; WO2023226733A9

Abstract

The invention discloses a method and a device for acquiring vehicle scene data, a storage medium and electronic equipment. Wherein the method comprises the following steps: acquiring target data determined by an automatic driving system of a vehicle during the running of the vehicle, wherein the target data comprises at least one of the following data: control instructions of the vehicle by the automatic driving system, perception data of the environment where the vehicle is located by the automatic driving system, positioning data of the vehicle determined by the automatic driving system, and predicted planning data predicted by the automatic driving system; determining whether the vehicle is in a target scene according to the target data; in the case that the vehicle is determined to be in the target scene, vehicle scene data of the vehicle in the target scene is collected. By adopting the technical scheme, the problem that the acquired vehicle scene data are less is solved.

Description

Vehicle scene data acquisition method and device, storage medium and electronic equipment

Technical Field

The invention relates to the field of communication, in particular to a method and a device for acquiring vehicle scene data, a storage medium and electronic equipment.

Background

With the rapid development of science and technology, the field of automatic driving of vehicles develops rapidly, but automatic driving can be divided into different levels, and higher-level automatic driving can adapt to more scenes, so that automatic driving can be realized without limiting scenes, namely, the development of a high-level automatic driving system takes the scenes without boundary limitation as verification assumptions and has the capability of covering complex environments, strange scenes, sudden scenes and the like.

The automatic driving algorithm needs to deal with many and complex scenes, the life cycle of the algorithm is long, and long-time iterative optimization is needed, but in the process of testing the automatic driving system, the automatic driving algorithm is tested according to a fixed test scene, so that a tester manually records problems under the condition that the automatic driving algorithm cannot cover the current working condition, the automatic driving algorithm is tested after optimizing software, the efficiency is low, and all problems that software cannot be found are caused due to the fact that some marginal scenes cannot be identified and tested. The existing scene data acquisition is artificially simulated scenes, and then the collection of the scenes is manually triggered by testers, so that the acquired scene data are less and lack of reality.

Aiming at the problem that the acquired vehicle scene data is less in the related technology, an effective solution is not provided at present.

Accordingly, there is a need for improvement in the related art to overcome the disadvantages of the related art.

Disclosure of Invention

In view of the above, the present invention provides a method, an apparatus, a storage medium, and an electronic device for acquiring vehicle scene data, so as to at least solve the technical problem of less acquired vehicle scene data.

In order to achieve the above object, in a first aspect, the present invention provides a method for acquiring vehicle scene data, the method including: acquiring target data determined by an automatic driving system of a vehicle during the running of the vehicle, wherein the target data comprises at least one of the following data: control instructions of the vehicle by the automatic driving system, perception data of the environment where the vehicle is located by the automatic driving system, positioning data of the vehicle determined by the automatic driving system, and predicted planning data predicted by the automatic driving system; determining whether the vehicle is in a target scene according to the target data; in the event that the vehicle is determined to be in the target scene, vehicle scene data of the vehicle in the target scene is collected.

In one exemplary embodiment, in the case where 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 abnormal driving scene when the vehicle is determined to be in an automatic driving mode and the control instruction indicates that the acceleration of the vehicle in a first direction is set to be a first acceleration, wherein the first direction is a forward direction of the vehicle, and the value of the first acceleration exceeds a first threshold value; under the conditions that the vehicle is determined to be in an automatic driving mode, the control instruction indicates that the acceleration of the vehicle in a second direction is set as a second acceleration, and the control instruction indicates that the change rate of the rotation angle of the tire of the vehicle is set as a target change rate of the rotation angle, the vehicle is determined to be in an abnormal driving scene, wherein the second acceleration exceeds a second threshold value, the target change rate of the rotation angle exceeds a third threshold value, and an included angle between the second direction and the second direction is a preset included angle; under the condition that the vehicle is determined to be in the manual driving mode, determining a target control instruction issued by a target object to the vehicle; comparing the target control instruction with the control instruction in the target data, and determining that the vehicle is in a scene with an abnormal prediction control instruction when the similarity between the target control instruction and the control instruction is smaller than a fourth threshold value; wherein the target scene comprises: the driving abnormal scene and the prediction control command abnormal scene.

In one exemplary embodiment, in the case where the target data includes the perception data, determining whether the vehicle is in a target scene according to the target data includes: determining that the vehicle is in an abnormal scene of a perception module under the condition that the type of a first obstacle perceived by the perception module of the automatic driving system is determined to change within a first preset time length according to the perception data; determining that the vehicle is in an abnormal scene of a perception module under the condition that the identification of the first obstacle is determined to change within a first preset time length according to the perception data; under the condition that the variation of the moving speed of the first obstacle in a first preset time length is determined to exceed a fifth threshold according to the perception data, the vehicle is determined to be in an abnormal scene of a perception module; determining that the vehicle is in an abnormal scene of the sensing module under the condition that the position of the second obstacle sensed by the sensing module is determined to change within a first preset time length according to the sensing data; determining that the vehicle is in an abnormal scene of a perception module under the condition that the position of a third obstacle perceived by the perception module is determined to be overlapped with the position of the vehicle according to the perception data; determining that the vehicle is in a preset scene under the condition that the perception data are preset perception data and the vehicle body data of the vehicle are preset vehicle body data; wherein the target scene comprises: the sensing module is used for sensing an abnormal scene, and the preset scene.

In an exemplary embodiment, in the case where the object data includes the positioning data, determining whether the vehicle is in an object scene from the object data includes: under the condition that the variation of the position of the vehicle in a second preset time length is determined to exceed a sixth threshold according to the positioning data, determining that the vehicle is in an abnormal scene of a positioning module; under the condition that the variation of the moving direction of the vehicle in the second preset time length is determined to exceed a seventh threshold according to the positioning data, determining that the vehicle is in an abnormal scene of a positioning module; determining that the vehicle is in an abnormal scene of a positioning module under the condition that the position of the vehicle is determined not to change within the second preset time period according to the positioning data, but the speed of the vehicle is a target speed; wherein the target scene comprises: and the positioning module is in an abnormal scene.

In one exemplary embodiment, where the target data includes the forecast planning data, determining from the target data whether the vehicle is in a target scene includes: determining similarity between the predicted movement state and a target movement state of a third obstacle when the predicted planning data is used for indicating the predicted movement state of the third obstacle, and determining that the vehicle is in an abnormal scene of a prediction planning module when the similarity is smaller than an eighth threshold, wherein the target movement state is a movement state obtained after the third obstacle is detected; determining feasibility of a planned movement trajectory of the vehicle if the predictive planning data is indicative of the planned movement trajectory, and determining that the vehicle is in a predictive planning module exception scenario if the feasibility is less than a ninth threshold; in the event that the predictive planning data is indicative of a planned movement trajectory of the vehicle, determining whether there is an obstacle on the planned movement trajectory, and in the event that it is determined that there is an obstacle on the planned movement trajectory, determining that the vehicle is in a predictive planning module exception scenario; determining that the vehicle is in a scene to be manually controlled in the case that the predictive planning data indicates that the autonomous driving system is unable to control the vehicle; wherein the target scene comprises: and the prediction planning module is used for predicting abnormal scenes and controlling the scenes to be manually controlled.

In one exemplary embodiment, collecting vehicle scene data of the vehicle in the target scene includes: acquiring body data of the vehicle within a preset time period, wherein the vehicle is in the target scene within the preset time period; acquiring chassis data of the vehicle in the preset time period; collecting process data and control instruction data generated by the automatic driving system in the preset time period of the vehicle; and acquiring perception data of the environment where the vehicle is located, which is determined by an image acquisition device and a radar sensor in the preset time period.

In an exemplary embodiment, after acquiring vehicle scene data of the vehicle in the target scene, the method further comprises: sending the vehicle scene data to a cloud server so that the cloud server adjusts an algorithm of the automatic driving system according to the vehicle scene data; and acquiring the target algorithm adjusted by the cloud server, and updating the algorithm of the automatic driving system into the target algorithm.

In a second aspect, the present invention further provides an apparatus for acquiring vehicle scene data, the apparatus comprising: the vehicle driving control device comprises an acquisition module, a display module and a control module, wherein the acquisition module is used for acquiring target data determined by an automatic driving system of a vehicle during the driving of the vehicle, and the target data comprises at least one of the following data: control instructions of the vehicle by the automatic driving system, perception data of the environment where the vehicle is located by the automatic driving system, positioning data of the vehicle determined by the automatic driving system, and predicted planning data predicted by the automatic driving system; a determination module for determining whether the vehicle is in a target scene according to the target data; the acquisition module is used for acquiring vehicle scene data of the vehicle in the target scene under the condition that the vehicle is determined to be in the target scene.

In a third aspect, the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a stored program, and when the program runs, a device where the computer-readable storage medium is located is controlled to execute the method for acquiring vehicle scene data in any one of the foregoing technical solutions.

In a fourth aspect, the present invention also provides an electronic device comprising one or more processors; a storage device, configured to store one or more programs, which when executed by the one or more processors, cause the one or more processors to implement a method for running a program, wherein the program is configured to execute the method for acquiring vehicle scene data according to any one of the above technical solutions.

According to the invention, in the process of vehicle driving, whether the vehicle is in a set target scene is automatically judged by the vehicle, and then data acquisition is triggered, because the capability of the vehicle for identifying the scene is stronger than that of the scene identified manually, more scenes can be identified by the vehicle, and the scenes in which the vehicle is located have diversity during driving, so that a large amount of real and diverse scene data can be collected, and the problem of less acquired vehicle scene data is solved.

In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:

fig. 1 is a block diagram of a hardware configuration of a computer terminal of a method for acquiring vehicle scene data according to an embodiment of the present invention;

FIG. 2 is a flow chart of a method of collecting vehicle scene data according to an embodiment of the invention;

FIG. 3 is a hardware architecture of a vehicle-end data acquisition unit according to an embodiment of the present invention;

FIG. 4 is a data acquisition trigger mechanism diagram (one) according to an embodiment of the present invention;

FIG. 5 is a schematic diagram of a data acquisition triggering scenario according to an embodiment of the present invention;

FIG. 6 is a data acquisition trigger diagram (two) according to an embodiment of the present invention;

fig. 7 is a block diagram of a configuration of a vehicle scene data acquisition apparatus according to an embodiment of the present invention.

Detailed Description

Specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings, but the present invention is not limited thereto.

It will be understood that various modifications may be made to the embodiments disclosed herein. Accordingly, the foregoing description should not be construed as limiting, but merely as exemplifications of embodiments. Other modifications will occur to those skilled in the art which are within the scope and spirit of the invention.

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

These and other characteristics of the invention will become apparent from the following description of a preferred form of embodiment, given as a non-limiting example, with reference to the accompanying drawings.

It should also be understood that, although the invention has been described with reference to some specific examples, a person of skill in the art shall certainly be able to achieve many other equivalent forms of the invention, having the characteristics as set forth in the claims and hence all coming within the field of protection defined thereby.

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

Specific embodiments of the present invention are described hereinafter with reference to the accompanying drawings; however, it is to be understood that the disclosed embodiments are merely exemplary of the invention, which can be embodied in various forms. Well-known and/or repeated functions and constructions are not described in detail to avoid obscuring the invention in unnecessary or unnecessary detail. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present invention in virtually any appropriately detailed structure.

It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in other sequences than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

The specification may use the phrases "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," which may each refer to one or more of the same or different embodiments in accordance with the invention.

The invention is further described with reference to the following figures and specific examples.

The method embodiments provided in the embodiments of the present application may be executed in a computer terminal or a similar computing device. Taking the example of running on a computer terminal, fig. 1 is a hardware structure block diagram of the computer terminal of the method for acquiring vehicle scene data according to the embodiment of the present invention. As shown in fig. 1, the computer terminal may include one or more processors 102 (only one is shown in fig. 1), wherein the processors 102 may include, but are not limited to, a Microprocessor (MPU) or a Programmable Logic Device (PLD), and a memory 104 for storing data, and in an exemplary embodiment, the computer terminal may further include a transmission device 106 for communication function and an input/output device 108. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration and is not intended to limit the structure of the computer terminal. For example, the computer terminal may also include more or fewer components than shown in FIG. 1, or have a different configuration with equivalent functionality to that shown in FIG. 1 or with more functionality than that shown in FIG. 1.

The memory 104 may be used to store a computer program, for example, a software program and a module of application software, such as a computer program corresponding to the method for acquiring vehicle scene data in the embodiment of the present invention, and the processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, so as to implement the method described above. The 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 from the processor 102, which may be connected to a computer terminal over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The transmission device 106 is used for receiving or transmitting data via a network. Specific examples of the network described above 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 (NIC), which can be connected to other Network devices through a base station so as to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.

The invention provides a vehicle scene data acquisition method, which can collect a large amount of real scene data when an automatic driving vehicle runs and efficiently screen out scenes with automatic driving requirements from a large amount of scene data.

In the present embodiment, a method for collecting vehicle scene data is provided, and fig. 2 is a flowchart of a method for collecting vehicle scene data according to an embodiment of the present invention, where the flowchart includes the following steps:

step S202: acquiring target data determined by an automatic driving system of a vehicle during the running of the vehicle, wherein the target data comprises at least one of the following data: control instructions of the vehicle by the automatic driving system, perception data of the environment where the vehicle is located by the automatic driving system, positioning data of the vehicle determined by the automatic driving system, and predicted planning data predicted by the automatic driving system;

as an optional example, the control instruction includes but is not limited to: the control method comprises the following steps of controlling the vehicle to accelerate, controlling the vehicle to brake and decelerate, controlling the vehicle to turn around, controlling the vehicle to turn on light and the like.

As an optional example, the above-mentioned perception data includes but is not limited to: the number, type, identification, speed, position, driving state, and the like of obstacles present around the vehicle are perceived.

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

As an alternative example, the forecast planning data includes, but is not limited to: a predicted moving state of the obstacle (for example, the obstacle moves in the xx direction at the x speed), a planned travel trajectory of the vehicle, and the like.

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

it should be noted that the target scenario is a scenario in which the tester wants to collect vehicle scenario data, and the target scenario includes, but is not limited to: the method comprises the following steps of driving abnormal scenes, prediction control instruction abnormal scenes, sensing module abnormal scenes, preset scenes defined by testers, positioning module abnormal scenes, prediction planning module abnormal scenes and scenes to be controlled manually. These scenarios will be specifically described below, and will not be described herein again.

Step S206: in the event that the vehicle is determined to be in the target scene, vehicle scene data of the vehicle in the target scene is collected.

As an optional example, acquiring vehicle scene data of the vehicle in the target scene may be implemented by:

acquiring body data of the vehicle within a preset time period, wherein the vehicle is in the target scene within the preset time period; acquiring chassis data of the vehicle in the preset time period; collecting process data and control instruction data generated by the automatic driving system in the preset time period of the vehicle; and acquiring perception data of the environment where the vehicle is located, which is determined by an image acquisition device and a radar sensor in the preset time period.

The vehicle scene data includes: body data, chassis data, process data generated by an automatic driving system, control instruction data and perception data.

Specifically, fig. 3 is a hardware architecture of a vehicle-end data acquisition unit according to an embodiment of the present invention, and vehicle scene data may be acquired by the vehicle-end data acquisition unit shown in fig. 3, where the vehicle-end data acquisition unit includes: the data acquisition triggering calculation unit and the data storage unit are connected with the data acquisition triggering calculation unit;

the input of the vehicle-end data acquisition unit comprises:

(1) the information such as body data, chassis data and the like of the incoming vehicle is transmitted through the gateway;

(2) process data and control instruction data generated by an autopilot control unit of the autopilot system;

(3) the vehicle passes through the perception data sensed by an image acquisition device (such as a camera) and a radar sensor (such as a laser radar).

As an optional example, after the step S206 is executed, the vehicle scene data is further sent to a cloud server, so that the cloud server adjusts an algorithm of the automatic driving system according to the vehicle scene data; and acquiring the target algorithm adjusted by the cloud server, and updating the algorithm of the automatic driving system into the target algorithm.

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

Through the steps, in the running process of the vehicle, whether the vehicle is in the set target scene or not is automatically judged through the vehicle, and then data acquisition is triggered.

As an optional example, in a case that the target data includes the control instruction, determining whether the vehicle is in a target scene according to the target data is implemented in the following first to third ways:

the first method is as follows: in a case where it is determined that the vehicle is in an automatic driving mode and the control instruction instructs to set an acceleration of the vehicle in a first direction as a first acceleration, which is a forward direction of the vehicle, a value of the first acceleration exceeding a first threshold, a target scene including: driving abnormal scenes;

it should be noted that, if the control instruction indicates that the acceleration of the vehicle in the first direction is set as the first acceleration, it indicates that the automatic driving system is to control the vehicle to suddenly brake, that is, the vehicle is currently in an extreme emergency state, that is, in an abnormal driving situation.

The second method comprises the following steps: under the conditions that the vehicle is determined to be in an automatic driving mode, the control instruction indicates that the acceleration of the vehicle in a second direction is set as a second acceleration, and the control instruction indicates that the change rate of the rotation angle of the tire of the vehicle is set as a target change rate of the rotation angle, the vehicle is determined to be in an abnormal driving scene, wherein the second acceleration exceeds a second threshold value, the target change rate of the rotation angle exceeds a third threshold value, and an included angle between the second direction and the second direction is a preset included angle;

if the control command instructs to set the acceleration of the vehicle in the second direction as the second acceleration and instructs to set the change rate of the rotation angle of the tire of the vehicle as the target change rate of the rotation angle, it indicates that the vehicle is suddenly steered by the automated driving, and the vehicle is in an abrupt extreme state, that is, in an abnormal driving scene.

The third method comprises the following steps: under the condition that the vehicle is determined to be in the manual driving mode, determining a target control instruction issued by a target object to the vehicle; comparing the target control instruction with the control instruction in the target data, and determining that the vehicle is in a scene with an abnormal prediction control instruction when the similarity between the target control instruction and the control instruction is smaller than a fourth threshold, wherein the target scene comprises: and predicting an abnormal scene of the control instruction.

It should be noted that, in the manual driving mode, the automatic driving system operates in a simulation mode, the control instruction issued by the automatic driving system does not directly control the vehicle, at this time, the difference between the operation behavior of the driver and the actual state of the vehicle and the control instruction of the automatic driving system is analyzed, and when the difference between the operation behavior and the actual state of the vehicle exceeds a set fourth threshold, it is determined that the vehicle is in a scene with an abnormal predicted control instruction.

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 indicates that the similarity between the target control instruction and the vehicle is smaller than the fourth threshold.

As an optional example, in a case that the target data includes the perception data, determining whether the vehicle is in a target scene according to the target data may be determined in the following four to nine ways:

the method four comprises the following steps: determining that the vehicle is in a sensing module abnormal scene under the condition that the type of a first obstacle sensed by a sensing module of the automatic driving system is determined to be changed within a first preset time length according to the sensing data, wherein the target scene comprises: sensing an abnormal scene of the module;

optionally, the first preset time period may be 1 second, 2 seconds, and the like, the first obstacle is a movable obstacle (for example, a pedestrian and a vehicle that can move on a road), and if the type of the first obstacle changes within the first preset time period (for example, the first obstacle starts to be a pedestrian and is followed by a vehicle), it is indicated that the type of the obstacle is sensed to jump, that is, the sensing module is abnormal, and the vehicle is in an abnormal scene of the sensing module.

The fifth mode is as follows: determining that the vehicle is in an abnormal scene of a perception module under the condition that the identification of the first obstacle is determined to change within a first preset time length according to the perception data;

it should be noted that, if the identifier of the first obstacle changes within the first preset time period (for example, the identifier of the first obstacle at the beginning is the vehicle 1, and then changes into the vehicle 2), it indicates that the perceived identifier of the obstacle jumps, that is, the perception module is abnormal, and the vehicle is in an abnormal scene of the perception module.

The method six: under the condition that the variation of the moving speed of the first obstacle in a first preset time length is determined to exceed a fifth threshold according to the perception data, the vehicle is determined to be in an abnormal scene of a perception module;

it should be noted that, if the variation of the moving speed of the first obstacle within the first preset time exceeds the fifth threshold, it indicates that the sensed speed of the obstacle jumps, that is, the sensing module is abnormal, and the vehicle is in an abnormal scene of the sensing module.

The method is as follows: determining that the vehicle is in an abnormal scene of the sensing module under the condition that the position of the second obstacle sensed by the sensing module is determined to change within a first preset time length according to the sensing data;

it should be noted that the second obstacle is a stationary obstacle (e.g., a tree, a house, etc.), and if the position of the second obstacle changes within the first preset time period, it indicates that the position of the stationary obstacle jumps, that is, the sensing module is abnormal, and the vehicle is in an abnormal scene of the sensing module.

The method eight: determining that the vehicle is in an abnormal scene of a perception module under the condition that the position of a third obstacle perceived by the perception module is determined to be overlapped with the position of the vehicle according to the perception data;

the third obstacle includes the first obstacle and the second obstacle.

The method is nine: determining that the vehicle is in a preset scene under the conditions that the perception data are preset perception data and the vehicle body data of the vehicle are preset vehicle body data, wherein the target scene comprises: and presetting a scene.

Alternatively, the autopilot system may be developed for a particular scenario, thus requiring data collection for a characteristic scenario. Specifically, the interested preset scene can be configured at the cloud server, the scene needing data acquisition is sent to the vehicle according to specific requirements, the vehicle identifies the scene when receiving the preset scene requirements, and the data record is triggered and uploaded to the cloud server. For example, the data of the automatic driving lane changing scene needs to be collected, the data can be sent to the vehicle end, the vehicle end recognizes the lane changing behavior of the vehicle according to the sensing data, and then the whole process data of lane changing is recorded and uploaded to the cloud storage application.

The preset scene can be comprehensively judged according to perception, positioning and high-precision map information, for example: (1) road type (high speed/city/garden …), shape (straight/curve/turn …), number of lanes, signal lights; (2) number, category, location, speed, etc. of obstacles; (3) current speed, acceleration, light state and the like of the vehicle. The preset sensing data includes, but is not limited to, the above (1), and the preset vehicle body data includes, but is not limited to, the above (3).

As an optional example, in a case where the object data includes the positioning data, determining whether the vehicle is in an object scene according to the object data may be determined in the following manner ten to twelve:

the method comprises the following steps: under the condition that the variation of the position of the vehicle in a second preset time length is determined to exceed a sixth threshold according to the positioning data, determining that the vehicle is in an abnormal positioning module scene, wherein the target scene comprises: the positioning module is in an abnormal scene;

optionally, the second preset time period may be 1 second or 2 seconds.

It should be noted that, if the variation of the position of the vehicle within the second preset time exceeds the sixth threshold (for example, the variation is changed from positioning beijing to positioning wuhan), it indicates that the position of the vehicle jumps, that is, the positioning module of the automatic driving system is abnormal, and the vehicle is in an abnormal scene of the positioning module.

The eleventh mode: under the condition that the variation of the moving direction of the vehicle in the second preset time length is determined to exceed a seventh threshold according to the positioning data, determining that the vehicle is in an abnormal scene of a positioning module;

it should be noted that, if the variation of the moving direction of the vehicle within the second preset time exceeds a seventh threshold (for example, the variation changes suddenly from forward driving to backward driving), it indicates that the heading of the vehicle jumps, that is, the positioning module of the automatic driving system is abnormal, and the vehicle is in an abnormal scene of the positioning module.

The method twelve: determining that the vehicle is in an abnormal scene of a positioning module under the condition that the position of the vehicle is determined not to change within the second preset time period according to the positioning data, but the speed of the vehicle is a target speed;

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

As an alternative example, in the case where the target data includes the prediction data, determining whether the vehicle is in a target scene according to the target data may be determined in the following manners thirteen to sixteenth:

a thirteenth mode: determining similarity between the predicted movement state and a target movement state of a third obstacle when the predicted data is used for indicating the predicted movement state of the third obstacle, and determining that the vehicle is in an abnormal scene of a prediction planning module when the similarity is smaller than an eighth threshold, wherein the target movement state is a movement state obtained after the third obstacle is detected, and the target scene comprises: predicting abnormal scenes of a planning module;

it should be noted that, if the difference between the predicted movement state of the third obstacle predicted by the automatic driving module and the detected actual movement state of the third obstacle is large, it indicates that the prediction planning module of the automatic driving system is abnormal, that is, the vehicle is in an abnormal scene of the prediction planning module.

The fourteen modes: determining feasibility of a planned movement trajectory of the vehicle if the predictive planning data is indicative of the planned movement trajectory, and determining that the vehicle is in a predictive planning module exception scenario if the feasibility is less than a ninth threshold;

a fifteenth mode: determining whether an obstacle is present on the planned movement trajectory if the predicted planning data is indicative of the planned movement trajectory of the vehicle, and determining that the vehicle is in a predicted planning module exception scenario if it is determined that an obstacle is present on the planned movement trajectory;

the method has the following sixteen steps: determining that the vehicle is in a scene to be manually controlled in the case that the predictive planning data indicates that the autonomous driving system is unable to control the vehicle, wherein a target scene comprises: and (5) waiting for manual scene control.

It is to be understood that the above-described embodiments are only a few, but not all, embodiments of the present invention. In order to better understand the above method for acquiring vehicle scene data, the following describes the above process with reference to an embodiment, but the method is not limited to the technical solution of the embodiment of the present invention, and specifically:

in order to realize full and effective collection of real scene data by an automatic driving vehicle, the invention provides a vehicle scene data collection method, which can trigger data collection in automatic driving and manual driving modes, specifically comprises automatic driving abnormity identification, man-vehicle difference analysis identification and interesting scene identification, and then uploads the data to a cloud server for storage and application.

Fig. 4 is a diagram (one) of a data collection triggering mechanism according to an embodiment of the present invention when a vehicle is in an automatic driving mode, as shown in fig. 4, the data collection triggering mechanism is divided into two cases,

(1) automatic driving anomaly scene recognition

The abnormal scene recognition of automatic driving mainly comprises five conditions, and fig. 5 is a schematic diagram of a data acquisition triggering scene according to an embodiment of the invention;

the concrete description is as follows:

(11) automatic driving abnormity take-over (equivalent to the scene to be controlled manually in the above embodiment)

When the automatic driving system runs, scenes which cannot be controlled by the system appear, the driver is prompted to take over manually, and data recording can be triggered for the scenes according to take-over signals.

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

When sudden braking occurs in the automatic driving operation, it is indicated that the vehicle is currently in an abnormal or extreme scene, data further analysis needs to be recorded, and data recording can be triggered according to the fact that the longitudinal (equivalent to the first direction in the above embodiment) acceleration exceeds a certain threshold.

When the automatic driving operation has a sharp turn, which indicates that the current situation is an abnormal or extreme situation, the data needs to be recorded for further analysis, and the data recording can be triggered according to the fact that 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.

(13) Sensing data exception (corresponding to the abnormal scene of the sensing module in the above embodiment)

Comprehensively judging historical sensing data for a period of time, and triggering data recording when the conditions of type jump, speed jump, obstacle ID jump, static obstacle position jump and the like of the sensed obstacle are found.

When the obstacle identification position has common sense errors such as overlapping with the vehicle, data recording is triggered.

(14) Positioning data abnormity (equivalent to positioning module abnormity scene in the above embodiment)

And comprehensively judging historical positioning data for a period of time, and triggering data recording when the conditions of own position jumping, own course jumping, speed and own position unchanged are found.

(15) Forecast planning data exception (equivalent to the scenario of exception of planning forecast module in the above embodiment)

And comprehensively judging historical predicted planning data for a period of time, and triggering data recording when the fact that the difference between the predicted obstacle and the actual obstacle truth value is large, a feasible path cannot be planned, the planned path collides with the obstacle and the like is found.

It should be noted that the automatic driving system generally includes a sensing, positioning, and prediction planning module, and when a functional module in the automatic driving system is abnormal, a data record is triggered.

(2) Scene of interest (corresponding to the preset scene in the above-described embodiment) identification

Automatic driving is developed for a specific scene, so data acquisition for a characteristic scene is required. As shown in fig. 4, the interested scene is configured at the cloud, the scene to be collected is issued to the vehicle according to the specific requirement, and the vehicle identifies the scene when receiving the requirement of the interested scene, triggers the data record, and uploads the data record to the cloud. For example, the data of the automatic driving lane changing scene needs to be collected, the data can be sent to the vehicle end, the vehicle end recognizes the lane changing behavior of the vehicle according to the sensing data, and then the whole process data of lane changing is recorded and uploaded to the cloud storage application.

It should be noted that the scene of interest can be comprehensively judged according to the sensing, positioning and high-precision map information:

(21) road type (high speed/city/garden …), shape (straight/curve/u-turn …), number of lanes, signal lights;

(22) number, category, location, speed, etc. of obstacles;

(23) current speed, acceleration, light state and the like of the vehicle.

Fig. 6 is a diagram (two) of a data acquisition triggering mechanism according to an embodiment of the present invention when a vehicle is in a manual driving mode, as shown in fig. 3, the data acquisition triggering mechanism is divided into three cases:

(1) human-vehicle difference analysis and identification

In the manual driving mode, the automatic driving system simulates operation, a control instruction of the automatic driving system does not directly control the vehicle, at the moment, difference analysis is carried out on the operation behavior of a driver, the actual state of the vehicle and the control instruction of the automatic driving system, and when the difference between the operation behavior and the actual state of the vehicle exceeds a set threshold value (when the difference between the operation behavior and the actual state of the vehicle exceeds the set threshold value, the vehicle is positioned in a scene with abnormal prediction control instruction), data recording is triggered.

(2) Automatic driving anomaly identification

In the manual driving mode, the automatic driving abnormity identification comprises perception data abnormity, positioning data abnormity and planning data abnormity, and the triggering methods of the three types of abnormity are consistent with those in the automatic driving mode.

(3) Scene of interest identification

The scene of interest recognition in the manual driving mode is consistent with that in the automatic driving mode.

It should be noted that, in order to realize effective collection of vehicle-end data of the automatic driving system, a trigger mechanism is designed to acquire and store data in both an automatic driving mode and a manual driving mode.

Under the automatic driving mode, the triggering of the abnormal automatic driving scene data and the triggering of the interested scene are designed, and the effective acquisition of the data under the automatic driving mode is improved. Under the manual driving mode, through the virtual operation of the automatic driving system, on the basis of collecting abnormal scene data and interesting scenes of automatic driving, the difference scene triggering of people and vehicles is increased, the difference between the automatic driving system and a manual driver can be quickly collected, the coverage of a vehicle-end scene is effectively improved, and the scene data support is provided for the optimized development of the automatic driving system.

In order to effectively collect data of an automatic driving abnormal scene, data collection is respectively triggered under five conditions, specifically, the automatic driving abnormal scene taking over, the automatic driving control command abnormality, the sensing data abnormality, the positioning data abnormality and the prediction planning data abnormality are included, and the abnormal scene can be effectively triggered integrally and in modules.

In order to realize the data acquisition of the specific scene, an interested scene identification module is designed, a cloud scene configuration function is designed, the interested scene can be issued remotely at the cloud, and the data acquisition of the specific scene can be quickly realized.

Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention or portions thereof contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes several instructions for enabling a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to execute the method of the embodiments of the present invention.

In this embodiment, a device for acquiring vehicle scene data is further provided, and the device is used to implement the foregoing embodiments and preferred embodiments, which have already been described and are not described again. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the devices described in the embodiments below are preferably implemented in software, implementations in hardware, or a combination of software and hardware are also possible and contemplated.

Fig. 7 is a block diagram of a configuration of an apparatus for acquiring vehicle scene data according to an embodiment of the present invention, the apparatus including:

an obtaining module 72, configured to obtain target data determined by an automatic driving system of a vehicle during driving of the vehicle, where the target data includes at least one of: control instructions of the vehicle by the automatic driving system, perception data of the environment where the vehicle is located by the automatic driving system, positioning data of the vehicle determined by the automatic driving system, and predicted planning data predicted by the automatic driving system;

a determination module 74 for determining whether the vehicle is in a target scene based on the target data;

and the acquisition module 76 is configured to acquire vehicle scene data of the vehicle in the target scene if it is determined that the vehicle is in the target scene.

Through the device, in the process of vehicle driving, whether the vehicle is in a set target scene or not is automatically judged through the vehicle, and then data acquisition is triggered.

In an exemplary embodiment, the determining module is configured to determine whether the vehicle is in a target scene if the target data includes the control instruction by: determining that the vehicle is in an abnormal driving scene when the vehicle is determined to be in an automatic driving mode and the control instruction indicates that the acceleration of the vehicle in a first direction is set to be a first acceleration, wherein the first direction is a forward direction of the vehicle, and the value of the first acceleration exceeds a first threshold value; or under the conditions that the vehicle is determined to be in an automatic driving mode, the control instruction indicates that the acceleration of the vehicle in the second direction is set as the second acceleration, and the control instruction indicates that the change rate of the rotation angle of the tire of the vehicle is set as the target change rate of the rotation angle, determining that the vehicle is in an abnormal driving scene, wherein the second acceleration exceeds a second threshold value, the target change rate of the rotation angle exceeds a third threshold value, and an included angle between the second direction and the second direction is a preset included angle; under the condition that the vehicle is determined to be in the manual driving mode, determining a target control instruction issued by a target object to the vehicle; comparing the target control instruction with the control instruction in the target data, and determining that the vehicle is in a scene with an abnormal prediction control instruction when the similarity between the target control instruction and the control instruction is smaller than a fourth threshold value; wherein the target scene comprises: the driving abnormal scene and the prediction control command abnormal scene.

In one exemplary embodiment, the determining module is configured to determine whether the vehicle is in a target scene if the target data includes the perception data by: determining that the vehicle is in an abnormal scene of a sensing module under the condition that the type of a first obstacle sensed by the sensing module of the automatic driving system is changed within a first preset time length according to the sensing data; or determining that the vehicle is in an abnormal scene of a perception module under the condition that the identification of the first obstacle is determined to change within a first preset time length according to the perception data; or under the condition that the variation of the moving speed of the first obstacle in the first preset time length is determined to exceed a fifth threshold according to the perception data, determining that the vehicle is in an abnormal scene of a perception module; or determining that the vehicle is in an abnormal scene of the sensing module under the condition that the position of the second obstacle sensed by the sensing module is determined to change within a first preset time length according to the sensing data; determining that the vehicle is in an abnormal scene of a perception module under the condition that the position of a third obstacle perceived by the perception module is determined to be overlapped with the position of the vehicle according to the perception data; determining that the vehicle is in a preset scene under the condition that the perception data are preset perception data and the vehicle body data of the vehicle are preset vehicle body data; wherein the target scene comprises: the sensing module is used for sensing an abnormal scene, and the preset scene.

In an exemplary embodiment, the determining module is configured to determine whether the vehicle is in a target scene by, in a case that the target data includes the positioning data: under the condition that the variation of the position of the vehicle in a second preset time length is determined to exceed a sixth threshold according to the positioning data, determining that the vehicle is in an abnormal scene of a positioning module; or determining that the vehicle is in an abnormal scene of a positioning module under the condition that the variation of the moving direction of the vehicle in the second preset time length is determined to exceed a seventh threshold according to the positioning data; or determining that the vehicle is in an abnormal scene of a positioning module under the condition that the position of the vehicle is determined not to change within the second preset time period according to the positioning data but the speed of the vehicle is the target speed; wherein the target scene comprises: and the positioning module is in an abnormal scene.

In an exemplary embodiment, the determining module is configured to determine whether the vehicle is in a target scene if the target data includes the forecast planning data by: determining similarity between the predicted movement state and a target movement state of a third obstacle when the predicted planning data is used for indicating the predicted movement state of the third obstacle, and determining that the vehicle is in an abnormal scene of a prediction planning module when the similarity is smaller than an eighth threshold, wherein the target movement state is a movement state obtained after the third obstacle is detected; or in the case that the predictive planning data is used to indicate a planned movement trajectory of the vehicle, determining feasibility of the planned movement trajectory, and in the case that the feasibility is less than a ninth threshold, determining that the vehicle is in a predictive planning module exception scenario; or in the case that the predicted planning data is used to indicate a planned movement trajectory of the vehicle, determining whether there is an obstacle on the planned movement trajectory, and in the case that it is determined that there is an obstacle on the planned movement trajectory, determining that the vehicle is in a predicted planning module abnormal scenario; or determining that the vehicle is in a scene to be manually controlled under the condition that the predictive planning data is used for indicating that the automatic driving system cannot control the vehicle; wherein the target scene comprises: and the prediction planning module is used for predicting abnormal scenes and controlling the scenes to be manually controlled.

In an exemplary embodiment, the determining module is configured to collect vehicle scene data of the vehicle in the target scene by: acquiring body data of the vehicle within a preset time period, wherein the vehicle is in the target scene within the preset time period; acquiring chassis data of the vehicle in the preset time period; collecting process data and control instruction data generated by the automatic driving system in the preset time period of the vehicle; and collecting the perception data of the environment where the vehicle is located, which is determined by the image collecting device and the radar sensor in the preset time period.

In an exemplary embodiment, the apparatus further includes: the processing module is used for acquiring vehicle scene data of the vehicle in the target scene, and then sending the vehicle scene data to a cloud server so that the cloud server can adjust an algorithm of the automatic driving system according to the vehicle scene data; and acquiring the target algorithm adjusted by the cloud server, and updating the algorithm of the automatic driving system into the target algorithm.

Embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program is arranged to perform the steps of any of the above-mentioned method embodiments when executed.

Alternatively, in the present embodiment, the storage medium may be configured to store a computer program for executing the steps of:

s1, acquiring target data determined by an automatic driving system of the vehicle in the driving process of the vehicle, wherein the target data comprises at least one of the following data: control instructions of the vehicle by the automatic driving system, perception data of the environment where the vehicle is located by the automatic driving system, positioning data of the vehicle determined by the automatic driving system, and predicted planning data predicted by the automatic driving system;

s2, determining whether the vehicle is in a target scene according to the target data;

s3, collecting vehicle scene data of the vehicle in the target scene under the condition that the vehicle is determined to be in the target scene.

In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to: various media capable of storing computer programs, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.

For specific examples in this embodiment, reference may be made to the examples described in the foregoing embodiments and exemplary implementations, and details of this embodiment are not repeated herein.

It should be noted that the storage media described above in the present invention can be computer readable signal media or computer readable storage media or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any storage medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

Embodiments of the present invention also provide an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the steps of any of the above method embodiments.

Optionally, in this embodiment, the processor may be configured to execute the following steps by a computer program:

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

For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary embodiments, and details of this embodiment are not repeated herein.

It will be apparent to those skilled in the art that the various modules or steps of the invention described above may be implemented using a general purpose computing device, they may be centralized on a single computing device or distributed across a network of computing devices, and they may be implemented using program code executable by the computing devices, such that they may be stored in a memory device and executed by the computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into various integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.

The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the principle of the present invention should be included in the protection scope of the present invention.

Claims

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

acquiring target data determined by an automatic driving system of a vehicle during the running of the vehicle, wherein the target data comprises at least one of the following data: control instructions of the vehicle by the automatic driving system, perception data of the environment where the vehicle is located by the automatic driving system, positioning data of the vehicle determined by the automatic driving system, and predicted planning data predicted by the automatic driving system;

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

in the event that the vehicle is determined to be in the target scene, vehicle scene data of the vehicle in the target scene is collected.

2. The method for acquiring vehicle scene data according to claim 1, wherein in the case where 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 abnormal driving scene when the vehicle is determined to be in an automatic driving mode and the control instruction indicates that the acceleration of the vehicle in a first direction is set to be a first acceleration, wherein the first direction is a forward direction of the vehicle, and the value of the first acceleration exceeds a first threshold value;

under the conditions that the vehicle is determined to be in an automatic driving mode, the control instruction indicates that the acceleration of the vehicle in a second direction is set to be a second acceleration, and the rotation angle change rate of tires of the vehicle is set to be a target rotation angle change rate, determining that the vehicle is in an abnormal driving scene, wherein the second acceleration exceeds a second threshold value, the target rotation angle change rate exceeds a third threshold value, and an included angle between the second direction and the second direction is a preset included angle;

under the condition that the vehicle is determined to be in the manual driving mode, determining a target control instruction issued by a target object to the vehicle; comparing the target control instruction with the control instruction in the target data, and determining that the vehicle is in a predicted control instruction abnormal scene under the condition that the similarity between the target control instruction and the control instruction is smaller than a fourth threshold value;

wherein the target scene comprises: the driving abnormal scene and the prediction control command abnormal scene.

3. The method for acquiring vehicle scene data according to claim 1, wherein in a case that the target data includes the perception data, determining whether the vehicle is in a target scene according to the target data comprises:

determining that the vehicle is in an abnormal scene of a perception module under the condition that the type of a first obstacle perceived by the perception module of the automatic driving system is determined to change within a first preset time length according to the perception data;

determining that the vehicle is in an abnormal scene of a perception module under the condition that the identification of the first obstacle is determined to change within a first preset time length according to the perception data;

under the condition that the variation of the moving speed of the first obstacle in a first preset time length is determined to exceed a fifth threshold according to the perception data, the vehicle is determined to be in an abnormal scene of a perception module;

determining that the vehicle is in an abnormal scene of the sensing module under the condition that the position of the second obstacle sensed by the sensing module is determined to change within a first preset time length according to the sensing data;

determining that the vehicle is in an abnormal scene of a perception module under the condition that the position of a third obstacle perceived by the perception module is determined to be overlapped with the position of the vehicle according to the perception data;

determining that the vehicle is in a preset scene under the condition that the perception data are preset perception data and the vehicle body data of the vehicle are preset vehicle body data;

wherein the target scene comprises: the sensing module is used for sensing abnormal scenes and the preset scenes.

4. The method for acquiring vehicle scene data according to claim 1, wherein in a case where the object data includes the positioning data, determining whether the vehicle is in an object scene according to the object data includes:

under the condition that the variation of the position of the vehicle in a second preset time length is determined to exceed a sixth threshold according to the positioning data, determining that the vehicle is in an abnormal scene of a positioning module;

under the condition that the variation of the moving direction of the vehicle in the second preset time length is determined to exceed a seventh threshold according to the positioning data, determining that the vehicle is in an abnormal scene of a positioning module;

determining that the vehicle is in an abnormal scene of a positioning module under the condition that the position of the vehicle is determined not to change within the second preset time period according to the positioning data, but the speed of the vehicle is a target speed;

wherein the target scene comprises: and the positioning module is in an abnormal scene.

5. The method of claim 1, wherein in the event that the target data includes the predictive planning data, determining whether the vehicle is in a target scene based on the target data comprises:

determining similarity between the predicted movement state and a target movement state of a third obstacle when the predicted planning data is used for indicating the predicted movement state of the third obstacle, and determining that the vehicle is in an abnormal scene of a prediction planning module when the similarity is smaller than an eighth threshold, wherein the target movement state is a movement state obtained after the third obstacle is detected;

determining feasibility of a planned movement trajectory of the vehicle if the predictive planning data is indicative of the planned movement trajectory, and determining that the vehicle is in a predictive planning module exception scenario if the feasibility is less than a ninth threshold;

determining whether an obstacle is present on the planned movement trajectory if the predicted planning data is indicative of the planned movement trajectory of the vehicle, and determining that the vehicle is in a predicted planning module exception scenario if it is determined that an obstacle is present on the planned movement trajectory;

determining that the vehicle is in a scene to be manually controlled in the case that the predictive planning data is used to indicate that the autonomous driving system cannot control the vehicle;

wherein the target scene comprises: and the prediction planning module is used for predicting abnormal scenes and controlling the scenes to be manually controlled.

6. The method for acquiring the vehicle scene data according to claim 1, wherein the acquiring the vehicle scene data of the vehicle in the target scene comprises:

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

acquiring chassis data of the vehicle in the preset time period;

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

and acquiring perception data of the environment where the vehicle is located, which is determined by an image acquisition device and a radar sensor in the preset time period.

7. The method of claim 1, wherein after collecting vehicle scene data of the vehicle in the target scene, the method further comprises:

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

and acquiring the target algorithm adjusted by the cloud server, and updating the algorithm of the automatic driving system into the target algorithm.

8. A vehicle scene data acquisition device, comprising:

the vehicle driving control device comprises an acquisition module, a display module and a control module, wherein the acquisition module is used for acquiring target data determined by an automatic driving system of a vehicle during the driving of the vehicle, and the target data comprises at least one of the following data: control instructions of the vehicle by the automatic driving system, perception data of the environment where the vehicle is located by the automatic driving system, positioning data of the vehicle determined by the automatic driving system, and predicted planning data predicted by the automatic driving system;

a determination module for determining whether the vehicle is in a target scene according to the target data;

the acquisition module is used for acquiring vehicle scene data of the vehicle in the target scene under the condition that the vehicle is determined to be in the target scene.

9. A computer-readable storage medium, comprising a stored program, wherein when the program runs, the computer-readable storage medium controls a device to execute the method for acquiring vehicle scene data according to any one of claims 1 to 7.

10. An electronic device, characterized in that the electronic device comprises one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement a method for running a program, wherein the program is arranged to perform the method of acquiring vehicle scene data of any one of claims 1 to 7 when running.