CN111028384B - Intelligent fault classification method and system for automatic driving vehicle - Google Patents
Intelligent fault classification method and system for automatic driving vehicle Download PDFInfo
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Abstract
The invention provides an intelligent fault classification method for an automatic driving vehicle, which comprises the following steps: s1, when the vehicle encounters a fault in the automatic driving process, judging whether the current vehicle exits the automatic driving state, if so, entering S2; if not, the process goes to S3; s2, if the vehicle exits the automatic driving state, collecting vehicle driving condition data and determining the type of the vehicle exiting the automatic driving based on the vehicle driving condition data; s3, if the vehicle does not exit the automatic driving state, detecting whether the map data of the vehicle in the fault state exists, if so, classifying the fault of the vehicle to obtain the fault category of the vehicle; if the map data does not exist, the failure occurring in the vehicle is determined as a map loss failure. The invention also provides an intelligent fault classification system, which can judge the reason of the fault of the automatically driven vehicle and the fault type of the vehicle, and is convenient for solving the fault of the vehicle according to the reason and the type of the fault.
Description
Technical Field
The invention belongs to the technical field of vehicles, and particularly relates to an intelligent fault classification method and system for an automatic driving vehicle.
Background
For over a century recently, the appearance of automobiles replaces the traditional transportation mode, so that the life of people is more convenient.
In recent years, with the development of science and technology, especially the rapid development of intelligent computing, the research of the automatic driving automobile technology becomes a focus of all industries. The '12 leading edge technologies for determining future economy' report issued by McKensin discusses the influence degree of the 12 leading edge technologies on the future economy and society, and analyzes and estimates the respective economic and social influence of the 12 technologies in 2025, wherein the automatic driving automobile technology is ranked at the 6 th position, and the influence of the automatic driving automobile technology in 2025 is estimated as follows: economic benefits are about $ 0.2-1.9 trillion per year, and social benefits can recover 3-15 million lives per year.
In general, systems for autonomous vehicles generally include three modules: the sensing module is equivalent to eyes of people, and the peripheral environment state is collected in real time through sensors such as a camera, a millimeter wave radar and a laser radar; the decision module is equivalent to the brain of a person and calculates the optimal driving decision plan according to the environmental state; and the third is an execution module, which is equivalent to hands and feet of a person and is used for executing decision-making commands and carrying out corresponding driving operations such as an accelerator, a brake, steering and the like.
The automatic driving vehicle is still in a research and development test stage at present, a plurality of instabilities still exist, especially a plurality of unpredictable sudden failures still occur in a complex scene, and the accurate and timely recording of the failures is an important technical means for propelling automatic driving;
however, at present, an automatic driving vehicle does not have a mature intelligent fault classification system to automatically classify the occurred faults, and only roughly classifies the occurred faults into two categories of manual takeover and non-manual takeover, so that a user cannot timely judge the fault type and cannot retrieve corresponding data after encountering the faults, and thus, analysis steps of the user during analyzing the faults are complicated, the period is prolonged, the faults are shelved, time and oil are consumed during each automatic driving of the vehicle, a large amount of resources are wasted, and the cost of the automatic driving vehicle during analyzing the faults is directly increased sharply.
Disclosure of Invention
The invention provides a fault intelligent classification method and system of an automatic driving vehicle, which aim to solve at least one technical problem in the prior art.
In a first aspect, an embodiment of the present invention provides an intelligent fault classification method for an autonomous vehicle, where the intelligent fault classification method includes the following steps:
s1, when the vehicle encounters a fault in the automatic driving process, judging whether the current vehicle exits the automatic driving state, if so, entering S2; if not, the process goes to S3;
s2, if the vehicle exits the automatic driving state, collecting vehicle driving condition data, and determining the type of the vehicle exiting the automatic driving based on the vehicle driving condition data;
s3, if the vehicle does not exit the automatic driving state, detecting whether the map data of the vehicle when the vehicle has a fault exists, if so, classifying the fault of the vehicle to obtain the fault category of the vehicle; if the map data does not exist, the failure occurring in the vehicle is determined as a map loss failure.
And further, judging whether the current vehicle exits the automatic driving state or not according to the power instruction data, the braking instruction data and the steering instruction data of the vehicle.
Further, the step S2 further includes the following sub-steps:
comparing the vehicle running condition data with a preset threshold range,
if the vehicle running condition data is not within the preset threshold value range, determining that the type of the vehicle quitting the automatic driving is non-automatic quitting;
and if the vehicle running condition data is within the preset threshold value range, determining that the type of the vehicle exiting the automatic driving is automatic exit.
Further, the vehicle running condition data comprises one or more of steering wheel rotation angle, braking force, running speed of the vehicle which does not reach a specified terminal point, and throttle force.
Further, the vehicle driving condition data not being within the preset threshold range includes one or more of the following situations:
the steering wheel rotation angle exceeds a preset steering wheel rotation angle threshold value within a preset time;
the braking force exceeds a preset braking force threshold value within a preset time;
the vehicle does not reach the designated terminal and the running speed is zero in the preset time;
the accelerator strength exceeds a preset accelerator strength threshold value within a preset time.
Further, the step S3 of classifying the vehicle fault to obtain the fault category of the vehicle includes the following sub-steps:
if the position information of the current vehicle is inconsistent with the position information in the map data when the vehicle has a fault, determining the fault of the vehicle as a map positioning fault;
if the vehicle does not acquire the sensing information of the obstacles around the vehicle or the types of the obstacles sensed by the vehicle are inconsistent with the types of the obstacles in the reference database, determining the fault of the vehicle as a sensing abnormal fault;
if the status display lamp of the vehicle sensor is not normally started, judging the fault of the vehicle as a hardware connection fault;
if the current driving state data of the vehicle is not consistent with the driving state data stored in advance, the fault occurring in the vehicle is judged as a fault with wrong driving operation.
In a second aspect, an embodiment of the present invention provides an intelligent fault classification system for an autonomous vehicle, where the intelligent fault classification system includes a judgment module, a determination module, a detection module, and a classification module; wherein the content of the first and second substances,
when the vehicle encounters a fault in the automatic driving process, the judging module is used for judging whether the current vehicle exits the automatic driving state;
if the vehicle has exited the autonomous driving state, the determination module performs the following: collecting vehicle running condition data, and determining the type of the vehicle exiting automatic driving based on the vehicle running condition data;
if the vehicle does not exit the automatic driving state, the detection module detects whether map data exist when the vehicle breaks down, and if the map data exist, the classification module classifies the faults of the vehicle to obtain fault categories of the vehicle; if the map data does not exist, the failure occurring in the vehicle is determined as a map loss failure.
Further, the determining module is configured to perform the following operations: and judging whether the current vehicle exits the automatic driving state or not according to the power command data, the braking command data and the steering command data of the vehicle.
Further, the determining module is configured to:
comparing the vehicle running condition data with a preset threshold range, and if the vehicle running condition data is not in the preset threshold range, determining that the type of the vehicle quitting the automatic driving is non-automatic quitting;
and if the vehicle running condition data is within the preset threshold value range, determining that the type of the vehicle exiting the automatic driving is automatic exit.
Further, the vehicle running condition data comprises one or more of steering wheel rotation angle, braking force, running speed of the vehicle not reaching a specified terminal point and accelerator force;
wherein the vehicle driving condition data does not include one or more of the following situations within a preset threshold range: the steering wheel rotation angle exceeds a preset steering wheel rotation angle threshold value within a preset time;
the braking force exceeds a preset braking force threshold value within a preset time;
the vehicle does not reach the designated terminal and the running speed is zero in the preset time;
the accelerator strength exceeds a preset accelerator strength threshold value within a preset time.
Further, if the detection module detects that the position information of the current vehicle is inconsistent with the position information in the map data when the vehicle has a fault, the classification module judges the fault of the vehicle as a map positioning fault category;
if the detection module detects that the vehicle does not acquire sensing information of obstacles around the vehicle or the type of the obstacles sensed by the vehicle is inconsistent with the type of the obstacles in the reference database, the classification module judges the fault occurring in the vehicle as a sensing abnormal fault type;
if the detection module detects that the state display lamp of the vehicle sensor is not normally started, the classification module judges the fault of the vehicle as a hardware connection fault type;
and if the detection module detects that the current driving state data of the vehicle is inconsistent with the pre-stored driving state data, the classification module judges the fault of the vehicle as a fault type with wrong driving operation.
The invention relates to a fault intelligent classification method and a fault intelligent classification system for an automatic driving vehicle, which are characterized by firstly judging whether the current vehicle exits from an automatic driving state; determining a type of the vehicle exiting autonomous driving based on the vehicle driving condition data if the vehicle has exited the autonomous driving state; if the vehicle does not exit the automatic driving state, detecting whether map data of the vehicle exists when the vehicle breaks down, and if the map data exists, classifying the faults of the vehicle to obtain fault categories of the vehicle; if the map data does not exist, the failure occurring in the vehicle is determined as a map loss failure. The intelligent fault classification method can judge the reason of the fault of the automatically driven vehicle and automatically detect the fault type of the vehicle without manual retrieval, statistics and classification, so that a user can conveniently solve the fault according to the fault reason and the fault type; because the invention can send different fault types to different users of the terminal equipment, the efficiency of solving the fault is more effectively accelerated.
Drawings
FIG. 1 is a schematic flow chart illustrating a method for intelligently classifying faults of an autonomous vehicle according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram illustrating intelligent fault classification for an autonomous vehicle according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The present invention is described in detail with reference to the embodiments shown in the drawings, but it should be understood that these embodiments are not intended to limit the present invention, and those skilled in the art should understand that functional, methodological, or structural equivalents or substitutions made by these embodiments are within the scope of the present invention.
Example one
Fig. 1 is a schematic flow chart of a fault intelligent classification method for an autonomous vehicle according to an embodiment of the present invention, and referring to fig. 1, the method includes the following steps:
s1, when the vehicle encounters a fault in the automatic driving process, judging whether the current vehicle exits the automatic driving state, if so, entering S2; if not, the process goes to S3;
specifically, the determination of whether the driving state of the current vehicle has exited the automatic driving state is determined by the following method:
judging whether the current vehicle exits the automatic driving state or not according to the power instruction data, the braking instruction data and the steering instruction data of the vehicle; and if the vehicle does not receive the data of the vehicle power instruction, the data of the brake instruction and the steering instruction, the vehicle exits from the automatic driving state.
S2: if the vehicle exits the automatic driving state, acquiring vehicle running condition data, and determining the type of the vehicle exiting the automatic driving based on the vehicle running condition data;
for example, the vehicle running condition data may be obtained from a transmission instruction of the vehicle, and include a steering wheel turning angle t, a braking force s, a running speed v at which the vehicle does not reach a specified destination, an accelerator force h, and the like.
If the vehicle running condition data is not within the preset threshold range, judging that the current vehicle quits the automatic driving and is subjected to manual intervention, namely judging that the type of the vehicle quitting the automatic driving is non-automatic quitting;
further, the vehicle driving condition data not being within the preset threshold range includes one or more of the following situations:
specifically, the steering wheel rotation angle t exceeds a preset steering wheel rotation angle threshold within a preset time (for example, within 3-10 s);
the braking force s exceeds a preset braking force threshold value within a preset time (for example, within 3-10 s);
the vehicle does not reach the specified end point and the running speed v is zero within a preset time (for example, 15-30 minutes);
the throttle force h exceeds a preset throttle force threshold within a preset time (e.g., within 3-10 s), and the like.
And if the vehicle running condition data is within the preset threshold range, determining that the vehicle quits the automatic driving without manual intervention, and judging that the vehicle quits the automatic driving as the type of automatic quitting.
S3, if the vehicle does not exit the automatic driving state, detecting whether the map data of the vehicle when the vehicle has a fault exists, if so, classifying the fault of the vehicle to obtain the fault category of the vehicle; if the map data does not exist, the failure occurring in the vehicle is determined as a map loss failure.
The map data of the vehicle when the fault occurs is the position information of the first vehicle (for example, vehicle GPS positioning information).
Specifically, the position information of the current vehicle is set as the position information of a second vehicle, and if the position information of the second vehicle is inconsistent with the position information of the first vehicle, the fault occurring in the vehicle is determined as a map positioning fault;
if the vehicle does not acquire the perception information of the obstacles around the vehicle (such as other running vehicle information around the current vehicle, information of vehicles parked at the roadside, roadblock information and the like) or the types of the obstacles perceived by the vehicle are inconsistent with the types of the obstacles in the reference database, judging the fault occurring in the vehicle as a perception abnormal fault;
if the state display lamp of the vehicle sensor (laser radar, millimeter wave radar and camera) is not normally started, judging the fault of the vehicle as a hardware connection fault;
if the driving state data of the vehicle is inconsistent with the driving state data stored in advance, judging the fault of the vehicle as a fault with wrong driving operation; the current driving state data of the vehicle comprises the position of the vehicle body of the vehicle on a road driving line and the distance between the vehicle and a previous vehicle in the driving process; if the vehicle body of the vehicle is not at the center of the road driving line, or the distance between the vehicle and the previous vehicle during the driving is larger than a preset safe distance threshold (for example, the preset safe distance threshold is 40 meters), the fault of the vehicle is determined as the fault type with wrong driving operation.
Furthermore, the type that the vehicle automatically quits the automatic driving is sent to the terminal equipment, so that the user can intuitively acquire the reason why the vehicle breaks down.
Further, the fault category of the vehicle is sent to the terminal device, so that a user can intuitively acquire the fault category of the vehicle at this time.
Furthermore, data in the faults of the vehicle (such as vehicle running condition data, map data when the vehicle is in fault, position information of the vehicle, sensing information of obstacles around the vehicle, status display lamp information of vehicle sensors (laser radar, millimeter wave radar and camera) and the like) are collected and analyzed, so that the faults can not reappear when the automatic driving vehicle encounters similar scenes, the recurrence phenomenon of the faults in the automatic driving process of the vehicle is reduced, compared with the fault classification method in the prior art, the fault intelligent classification method and the fault intelligent classification system for the automatic driving vehicle have the characteristics of easiness in judging fault reasons and types, easiness in summarizing the fault reasons and types and easiness in transmitting data when the vehicle breaks down.
Example two
The fault intelligent classification system for the automatic driving vehicle provided by the embodiment comprises a judgment module, a determination module, a detection module and a classification module; wherein the content of the first and second substances,
when the vehicle encounters a fault in the automatic driving process, the judging module is used for judging whether the current vehicle exits the automatic driving state;
if the vehicle has exited the autonomous driving state, the determination module performs the following: collecting vehicle running condition data, and determining the type of the vehicle exiting automatic driving based on the vehicle running condition data;
if the vehicle does not exit the automatic driving state, the detection module detects whether map data exist when the vehicle breaks down, and if the map data exist, the classification module classifies the faults of the vehicle to obtain fault categories of the vehicle; if the map data does not exist, the failure occurring in the vehicle is determined as a map loss failure.
Further, the determining module is configured to perform the following operations: and judging whether the current vehicle exits the automatic driving state or not according to the power command data, the braking command data and the steering command data of the vehicle.
Further, the determining module is configured to:
comparing the vehicle running condition data with a preset threshold range, and if the vehicle running condition data is not in the preset threshold range, determining that the type of the vehicle quitting the automatic driving is non-automatic quitting;
and if the vehicle running condition data is within the preset threshold value range, determining that the type of the vehicle exiting the automatic driving is automatic exit.
Further, the vehicle running condition data comprises one or more of steering wheel rotation angle, braking force, running speed of the vehicle not reaching a specified terminal point and accelerator force;
wherein the vehicle driving condition data does not include one or more of the following situations within a preset threshold range: the steering wheel rotation angle exceeds a preset steering wheel rotation angle threshold value within a preset time;
the braking force exceeds a preset braking force threshold value within a preset time;
the vehicle does not reach the designated terminal and the running speed is zero in the preset time;
the accelerator strength exceeds a preset accelerator strength threshold value within a preset time.
Further, if the detection module detects that the position information of the current vehicle is inconsistent with the position information in the map data when the vehicle has a fault, the classification module judges the fault of the vehicle as a map positioning fault category;
if the detection module detects that the vehicle does not acquire sensing information of obstacles around the vehicle or the type of the obstacles sensed by the vehicle is inconsistent with the type of the obstacles in the reference database, the classification module judges the fault occurring in the vehicle as a sensing abnormal fault type;
if the detection module detects that the state display lamp of the vehicle sensor is not normally started, the classification module judges the fault of the vehicle as a hardware connection fault type;
and if the detection module detects that the current driving state data of the vehicle is inconsistent with the pre-stored driving state data, the classification module judges the fault of the vehicle as a fault type with wrong driving operation.
The specific implementation process of the intelligent fault classification system in this embodiment is consistent with the specific implementation manner of each step in the intelligent fault classification method in the first embodiment, and details are not repeated here.
EXAMPLE III
Fig. 3 is a schematic structural diagram of an embodiment of an electronic device according to the present invention, and referring to fig. 3, in the embodiment, an electronic device is provided, including but not limited to an electronic device such as a smart phone, a fixed phone, a tablet computer, a notebook computer, a wearable device, and the like, and the electronic device includes: a processor and a memory, said memory storing computer readable instructions, said computer readable instructions when executed by said processor implementing the fault intelligent classification method of the present invention as described above.
Example four
In the present embodiment, a computer-readable storage medium is provided, which may be a ROM (e.g., read only memory, FLASH memory, transfer device, etc.), an optical storage medium (e.g., CD-ROM, DVD-ROM, paper card, etc.), a magnetic storage medium (e.g., magnetic tape, magnetic disk drive, etc.), or other types of program storage; the computer-readable storage medium has stored thereon a computer program which, when executed by a processor or a computer, performs the above-described fault intelligent classification method of the present invention.
The invention has the beneficial effects that:
the intelligent fault classification method and system for the automatic driving vehicle can judge the reason of the fault of the automatic driving vehicle, automatically detect the fault type of the vehicle, do not need manual retrieval statistics and classification, and are convenient for a user to solve the fault according to the fault reason and the fault type; the invention can send different fault types to different users of the terminal equipment, thereby more effectively accelerating the efficiency of solving the fault.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (7)
1. An intelligent fault classification method for an autonomous vehicle, the intelligent fault classification method comprising the steps of:
s1, when the vehicle encounters a fault in the automatic driving process, judging whether the current vehicle exits the automatic driving state, if so, entering S2; if not, the process goes to S3;
s2, if the vehicle exits the automatic driving state, collecting vehicle driving condition data, and determining the type of the vehicle exiting the automatic driving based on the vehicle driving condition data;
s3, if the vehicle does not exit the automatic driving state, detecting whether the map data of the vehicle when the vehicle has a fault exists, if so, classifying the fault of the vehicle to obtain the fault category of the vehicle; if the map data does not exist, judging the fault of the vehicle as a map loss fault;
wherein, whether the current vehicle exits the automatic driving state is judged by the following method:
judging whether the current vehicle exits from the automatic driving state or not according to the power instruction data, the braking instruction data and the steering instruction data of the vehicle, and judging that the vehicle exits from the automatic driving state if the vehicle does not receive the power instruction data and the braking instruction data and does not receive the steering instruction data;
the step S3 of classifying the fault occurred in the vehicle to obtain the fault category of the vehicle includes the following sub-steps:
if the position information of the current vehicle is inconsistent with the position information in the map data when the vehicle has a fault, determining the fault of the vehicle as a map positioning fault;
if the vehicle does not acquire the sensing information of the obstacles around the vehicle or the types of the obstacles sensed by the vehicle are inconsistent with the types of the obstacles in the reference database, determining the fault of the vehicle as a sensing abnormal fault;
if the status display lamp of the vehicle sensor is not normally started, judging the fault of the vehicle as a hardware connection fault;
if the current driving state data of the vehicle is not consistent with the driving state data stored in advance, the fault occurring in the vehicle is judged as a fault with wrong driving operation.
2. The intelligent fault classification method according to claim 1, wherein the step S2 further comprises the following sub-steps:
comparing the vehicle running condition data with a preset threshold range,
if the vehicle running condition data is not within the preset threshold value range, determining that the type of the vehicle quitting the automatic driving is non-automatic quitting;
and if the vehicle running condition data is within the preset threshold value range, determining that the type of the vehicle exiting the automatic driving is automatic exit.
3. The intelligent fault classification method according to claim 2, wherein the vehicle driving condition data comprises one or more of steering wheel rotation angle, braking force, driving speed of the vehicle not reaching a specified end point, and throttle force.
4. The intelligent fault classification method according to claim 3, wherein the vehicle driving condition data not being within a preset threshold range comprises one or more of the following:
the steering wheel rotation angle exceeds a preset steering wheel rotation angle threshold value within a preset time;
the braking force exceeds a preset braking force threshold value within a preset time;
the vehicle does not reach the designated terminal and the running speed is zero in the preset time;
the accelerator strength exceeds a preset accelerator strength threshold value within a preset time.
5. The intelligent fault classification system of the automatic driving vehicle is characterized by comprising a judgment module, a determination module, a detection module and a classification module; wherein the content of the first and second substances,
when the vehicle encounters a fault in the automatic driving process, the judging module is used for judging whether the current vehicle exits the automatic driving state;
if the vehicle has exited the autonomous driving state, the determination module performs the following: collecting vehicle running condition data, and determining the type of the vehicle exiting automatic driving based on the vehicle running condition data;
if the vehicle does not exit the automatic driving state, the detection module detects whether map data exist when the vehicle breaks down, and if the map data exist, the classification module classifies the faults of the vehicle to obtain fault categories of the vehicle; if the map data does not exist, judging the fault of the vehicle as a map loss fault;
wherein the judging module is used for executing the following operations: judging whether the current vehicle exits from the automatic driving state or not according to the power instruction data, the braking instruction data and the steering instruction data of the vehicle, and judging that the vehicle exits from the automatic driving state if the vehicle does not receive the power instruction data and the braking instruction data and does not receive the steering instruction data;
the classification module is configured to perform the following operations:
if the detection module detects that the position information of the current vehicle is inconsistent with the position information in the map data when the vehicle breaks down, the classification module judges the fault of the vehicle as a map positioning fault category;
if the detection module detects that the vehicle does not acquire sensing information of obstacles around the vehicle or the type of the obstacles sensed by the vehicle is inconsistent with the type of the obstacles in the reference database, the classification module judges the fault occurring in the vehicle as a sensing abnormal fault type;
if the detection module detects that the state display lamp of the vehicle sensor is not normally started, the classification module judges the fault of the vehicle as a hardware connection fault type;
and if the detection module detects that the current driving state data of the vehicle is inconsistent with the pre-stored driving state data, the classification module judges the fault of the vehicle as a fault type with wrong driving operation.
6. The fault intelligent classification system of claim 5, wherein the determination module is configured to:
comparing the vehicle running condition data with a preset threshold range, and if the vehicle running condition data is not in the preset threshold range, determining that the type of the vehicle quitting the automatic driving is non-automatic quitting;
and if the vehicle running condition data is within the preset threshold value range, determining that the type of the vehicle exiting the automatic driving is automatic exit.
7. The fault intelligent classification system of claim 6, wherein the vehicle driving condition data comprises one or more of steering wheel rotation angle, brake force, vehicle speed of driving without reaching a specified end point, throttle force;
wherein the vehicle driving condition data does not include one or more of the following situations within a preset threshold range: the steering wheel rotation angle exceeds a preset steering wheel rotation angle threshold value within a preset time;
the braking force exceeds a preset braking force threshold value within a preset time;
the vehicle does not reach the designated terminal and the running speed is zero in the preset time;
the accelerator strength exceeds a preset accelerator strength threshold value within a preset time.
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| CN111667605B (en) * | 2020-06-10 | 2022-07-19 | 阿波罗智能技术(北京)有限公司 | Automatic driving test data storage method and device and electronic equipment |
| CN111935250B (en) * | 2020-07-24 | 2022-11-01 | 上海轩邑新能源发展有限公司 | Automatic driving data classification transmission method and system |
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