CN115123304A

CN115123304A - Fault tracking method, device, medium and chip

Info

Publication number: CN115123304A
Application number: CN202210886284.3A
Authority: CN
Inventors: 叶剑武; 何亮亮; 李育桥
Original assignee: Xiaomi Automobile Technology Co Ltd
Current assignee: Xiaomi Automobile Technology Co Ltd
Priority date: 2022-07-26
Filing date: 2022-07-26
Publication date: 2022-09-30

Abstract

The disclosure relates to a fault tracking method, a fault tracking device, a medium and a chip, belongs to the field of vehicles, and can effectively track an automatic driving fault. A fault tracking method, comprising: acquiring source information of input information of the element parts related to automatic driving operation and execution duration of each operation of each element part; and tracking the fault of the automatic driving based on the source information and the execution duration.

Description

Fault tracking method, device, medium and chip

Technical Field

The present disclosure relates to the field of vehicles, and in particular, to a method, an apparatus, a medium, and a chip for tracking a fault.

Background

The operation of automatic driving can be completed only by the mutual cooperation of a plurality of sensors and a plurality of automatic driving algorithms. Since the operation of the automatic driving involves numerous sensors and automatic driving algorithms, an abnormality occurs in the operation of the automatic driving during a certain period of time, making it difficult to investigate the root cause of the operation abnormality.

Disclosure of Invention

To overcome the problems in the related art, the present disclosure provides a fault tracking method, apparatus, medium, and chip.

According to a first aspect of embodiments of the present disclosure, there is provided a fault tracking method, including: acquiring source information of input information of the element parts related to automatic driving operation and execution duration of each operation of each element part; and tracking the fault of the automatic driving based on the source information and the execution duration.

Optionally, the source information comprises which upstream meta-component the input information of the meta-component originates from and the input information was successfully generated by the upstream meta-component after the second execution.

Optionally, the tracking the fault of the automatic driving based on the source information and the execution duration includes: determining that a communication failure has occurred between the meta-component and the upstream meta-component in a case where the source information indicates that the meta-component has not received the input information from the upstream meta-component.

Optionally, the tracking the fault of the automatic driving based on the source information and the execution duration includes: and determining that the upstream element has a fault when the source information indicates that the execution times of the upstream element exceed a preset time.

Optionally, the tracking the fault of the automatic driving based on the source information and the execution duration includes: and if the execution time length of each operation of the element component exceeds the preset execution time length, determining that the element component has a fault.

Optionally, the method further comprises: and determining at least one of the average execution time length, the maximum execution time length, the minimum execution time length and the P90 time consumption of each meta-component for executing the operation according to the execution time length of each operation of each meta-component.

Optionally, the method further comprises: analyzing the source information and the execution duration into data in a set format; and outputting the data with the set format to a user.

According to a second aspect of the embodiments of the present disclosure, there is provided a fault tracking apparatus, including: the system comprises an acquisition module, a judgment module and a display module, wherein the acquisition module is used for acquiring source information of input information of the element related to automatic driving operation and execution duration of each operation of each element; and the tracking module is used for tracking the fault of automatic driving based on the source information and the execution duration.

According to a third aspect of the embodiments of the present disclosure, there is provided a fault tracking apparatus including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to perform the steps of the method according to any one of the first aspect of the present disclosure.

According to a fourth aspect of embodiments of the present disclosure, there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of the method according to any one of the first aspects of the present disclosure.

According to a fifth aspect of embodiments of the present disclosure, there is provided a chip comprising a processor and an interface; the processor is configured to read instructions to perform a method according to any one of the first aspect of the present disclosure.

By adopting the technical scheme, the source information of the input information of the element parts related to the automatic driving operation and the execution time length of each operation of each element part can be obtained, the fault of the automatic driving can be tracked based on the source information and the execution time length, namely, the element parts can be accurately positioned through the source information and the execution time length, the execution conditions of each module related to the automatic driving can be effectively analyzed and checked, and the element parts where the fault of the automatic driving occurs can be accurately determined.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.

FIG. 1 is a flow chart illustrating a fault tracking method in accordance with an exemplary embodiment.

FIG. 2 is yet another flow chart illustrating a fault tracking method in accordance with an exemplary embodiment.

FIG. 3 is yet another flow chart illustrating a fault tracking method in accordance with an exemplary embodiment.

FIG. 4 is a block diagram illustrating a fault tracking device in accordance with an exemplary embodiment.

FIG. 5 is yet another block diagram illustrating a fault tracking device in accordance with an exemplary embodiment.

FIG. 6 is yet another block diagram illustrating a fault tracking device in accordance with an exemplary embodiment.

FIG. 7 is a functional block diagram schematic of a vehicle, shown in an exemplary embodiment.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.

It should be noted that all the actions of acquiring signals, information or data in the present application are performed under the premise of complying with the corresponding data protection regulation policy of the country of the location and obtaining the authorization given by the owner of the corresponding device.

FIG. 1 is a flow chart illustrating a fault tracking method in accordance with an exemplary embodiment. The fault tracking method can be applied to an autonomous vehicle. As shown in fig. 1, the fault tracing method includes the following steps S11 and S12.

In step S11, the source information of the input information of the components involved in the automatic driving operation and the execution time period of each operation of each component are acquired.

The operation of autonomous driving involves a large number of sensors, a large number of autonomous driving algorithm modules. The sensors and the automatic driving algorithm modules form a directed relational graph according to a certain data dependency relationship and data circulation, namely, each sensor and each automatic driving algorithm module are nodes of the directed graph, and output data to input is directed edges of the directed graph.

For example, in the automatic driving process, a camera detects that a person is in front of the vehicle, the detection result is transmitted to a path planning algorithm, then the path planning algorithm plans a path according to the detection result of the camera, and then a subsequent driving algorithm controls the vehicle to automatically run according to the path planning result of the path planning algorithm. The camera, the path planning algorithm and the driving algorithm belong to nodes in the directed graph, and the directed edges of the directed graph are formed from the output of the camera to the input of the path planning algorithm, and the like.

In some embodiments, the source information may include which upstream meta-component the input information for the meta-component originated from and the input information was successfully generated by the upstream meta-component for the second execution. Still taking the foregoing example of the camera as an example, for the path planning algorithm, the source information of the path planning algorithm includes that the input information of the path planning algorithm (i.e., the image of the person in front acquired by the camera) comes from the camera, and that the input information of the path planning algorithm (i.e., the image of the person in front acquired by the camera) is successfully acquired after the camera performs the image capturing for the nth time, for example, during the image capturing period, the image is successfully acquired after the camera performs the image capturing operation for the nth time, where N is greater than or equal to 1, that is, in the first N-1 image capturing operations, the image is not successfully acquired although the camera performs the image capturing operation.

In some embodiments, the execution duration of each operation of each meta-component refers to the duration that each meta-component takes to perform its operation to be executed a single time. Still taking the example of the camera described above as an example, for the camera, the execution time length thereof refers to the time length taken for the camera to perform one-time image capturing.

In step S12, the failure of the automatic driving is tracked based on the source information and the execution time period.

In some embodiments, a determination is made that a communication failure has occurred between the meta-component and the upstream meta-component in the event that the source information indicates that the meta-component has not received input information from the upstream meta-component. Still taking the example of the camera described above as an example, if the source information indicates that the path planning module has not received images from the camera, this indicates that the information transmission between the camera and the path planning module has failed.

In some embodiments, it is determined that the upstream meta-component has a failure when the source information indicates that the number of executions of the upstream meta-component exceeds a preset number. Still taking the example of the camera described above as an example, assuming that the source information indicates that the input information received by the path planning algorithm is successfully acquired only after the camera performs 5 camera operations (that is, in the previous 4 camera operations, although the camera performs all the camera operations, none of the camera successfully acquires an image), and the preset fault-tolerant execution time is 3 times (that is, if the camera performs 3 camera operations continuously and does not acquire an image), it may be determined that the camera has a fault because the camera has executed more than the preset time when the camera successfully acquires an image.

In some embodiments, it is determined that the meta-component has failed if the execution time of each operation of the meta-component exceeds a preset execution time. The example of the camera described above is still taken as an example. Assuming that the camera is set to perform 10 times of image pickup in one second from the time line, and the execution time of each image pickup operation is 100ms, if the execution time of a certain image pickup operation of the camera is 150ms, the camera is considered to have a fault, because the execution time of the camera exceeds the requirement of 100ms, the next operation is affected.

FIG. 2 is yet another flow chart illustrating a fault tracking method in accordance with an exemplary embodiment. As shown in fig. 2, on the basis of fig. 1, the fault tracing method further includes step S13: and determining at least one of the average execution time length, the maximum execution time length, the minimum execution time length and the P90 time consumption of each meta-component for executing the operation according to the execution time length of each operation of each meta-component.

Still by taking the example of the camera described above as an example, according to the execution duration of each shooting operation of the camera in a period of time, the average execution duration, the maximum execution duration, the minimum execution duration and the consumed time of P90 of the camera in the period of time can be determined.

By adopting the technical scheme, at least one of the average execution time length, the maximum execution time length, the minimum execution time length and the P90 time consumption of each element can be determined according to the execution time length of each operation of each element, so that the performance of each element can be evaluated by utilizing the information such as the average execution time length, the maximum execution time length, the minimum execution time length and the P90 time consumption, the performance of each element involved in automatic driving can be analyzed accurately, and the improvement of automatic driving is facilitated.

FIG. 3 is yet another flow chart illustrating a fault tracking method in accordance with an exemplary embodiment. As shown in fig. 3, on the basis of fig. 1, the fault tracing method further includes steps S14 and S15.

In step S14, the source information and the execution time length are parsed into data of a set format.

The setting format of the parsed data may be set according to an actual scene, for example, the parsed data may be a file in an XML format, a file in a PDF format, or the like.

In step S15, the data in the set format is output to the user.

For example, if the file is in XML format, the data may be loaded through a web page.

By adopting the technical scheme, the source information and the execution duration can be analyzed into the data with the set format and output to the user, so that the user can know the execution conditions of each element of the automatic driving, and the automatic driving fault source can be analyzed and checked.

FIG. 4 is a block diagram illustrating a fault tracking device in accordance with an exemplary embodiment. The fault tracking device can be applied to an automatic driving vehicle. As shown in fig. 4, the fault tracking apparatus includes: an obtaining module 41, configured to obtain source information of input information of a component related to automatic driving operation and an execution duration of each operation of the component; and the tracking module 42 is used for tracking the fault of the automatic driving based on the source information and the execution duration.

Optionally, in case the source information indicates that the meta-component has not received the input information from the upstream meta-component, the tracing module 42 determines that a communication failure has occurred between the meta-component and the upstream meta-component.

Optionally, the tracking module 42 determines that the upstream meta-component has a fault when the source information indicates that the number of executions of the upstream meta-component exceeds a preset number.

Alternatively, the tracking module 42 determines that the component has a fault if the execution time of each operation of the component exceeds the preset execution time.

FIG. 5 is yet another block diagram illustrating a fault tracking device in accordance with an exemplary embodiment. The fault tracking apparatus further includes a determining module 43 for determining at least one of an average execution time, a maximum execution time, a minimum execution time and a P90 time for each meta-component to execute the operation according to the execution time of each meta-component per operation.

FIG. 6 is yet another block diagram illustrating a fault tracking device in accordance with an exemplary embodiment. As shown in fig. 6, the fault tracking apparatus further includes: the analysis module 44 is configured to analyze the source information and the execution duration into data in a set format; and an output module 45, configured to output the data in the set format to a user.

With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.

The present disclosure also provides a fault tracking device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to perform the steps of the method according to the present disclosure.

The present disclosure also provides a computer readable storage medium having stored thereon computer program instructions, characterized in that the program instructions, when executed by a processor, implement the steps of the method according to the present disclosure.

The present disclosure also provides a chip, which includes a processor and an interface; the processor is configured to read instructions to perform the steps of the method according to the present disclosure. The chip may be an Integrated Circuit (IC). The chip may include, but is not limited to, the following categories: a GPU (Graphics Processing Unit), a CPU (Central Processing Unit), an FPGA (Field Programmable Gate Array), a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an SOC (System on Chip, SOC, System on Chip, or System on Chip), and the like. The integrated circuit or chip may be configured to execute executable instructions (or code) to implement the fault tracking method described above. Where the executable instructions may be stored in the integrated circuit or chip or may be retrieved from another device or apparatus, for example, where the integrated circuit or chip includes a processor, a memory, and an interface for communicating with other devices. The executable instructions may be stored in the memory, and when executed by the processor, implement the fault tracking method described above; alternatively, the integrated circuit or chip may receive executable instructions through the interface and transmit the executable instructions to the processor for execution, so as to implement the fault tracking method described above.

Referring to fig. 7, fig. 7 is a functional block diagram of a vehicle 600 according to an exemplary embodiment. The vehicle 600 may be configured in a fully or partially autonomous driving mode. For example, the vehicle 600 may acquire environmental information of its surroundings through the sensing system 620 and derive an automatic driving strategy based on an analysis of the surrounding environmental information to implement full automatic driving, or present the analysis result to the user to implement partial automatic driving.

Vehicle 600 may include various subsystems such as infotainment system 610, perception system 620, decision control system 630, drive system 640, and computing platform 650. Alternatively, vehicle 600 may include more or fewer subsystems, and each subsystem may include multiple components. In addition, each of the sub-systems and components of the vehicle 600 may be interconnected by wire or wirelessly.

In some embodiments, the infotainment system 610 may include a communication system 611, an entertainment system 612, and a navigation system 613.

The communication system 611 may comprise a wireless communication system that may communicate wirelessly with one or more devices, either directly or via a communication network. For example, the wireless communication system may use 3G cellular communication, such as CDMA, EVD0, GSM/GPRS, or 4G cellular communication, such as LTE. Or 5G cellular communication. The wireless communication system may communicate with a Wireless Local Area Network (WLAN) using WiFi. In some embodiments, the wireless communication system may communicate directly with the device using an infrared link, bluetooth, or ZigBee. Other wireless protocols, such as various vehicular communication systems, for example, a wireless communication system may include one or more Dedicated Short Range Communications (DSRC) devices that may include public and/or private data communications between vehicles and/or roadside stations.

The entertainment system 612 may include a display device, a microphone, and a sound box, and a user may listen to a broadcast in the car based on the entertainment system, playing music; or the mobile phone is communicated with the vehicle, the screen projection of the mobile phone is realized on the display equipment, the display equipment can be in a touch control mode, and a user can operate the display equipment by touching the screen.

In some cases, the voice signal of the user may be acquired through a microphone, and certain control of the vehicle 600 by the user, such as adjusting the temperature in the vehicle, etc., may be implemented according to the analysis of the voice signal of the user. In other cases, music may be played to the user through a sound.

The navigation system 613 may include a map service provided by a map provider to provide navigation of a route of travel for the vehicle 600, and the navigation system 613 may be used in conjunction with a global positioning system 621 and an inertial measurement unit 622 of the vehicle. The map service provided by the map provider can be a two-dimensional map or a high-precision map.

The sensing system 620 may include several types of sensors that sense information about the environment surrounding the vehicle 600. For example, the sensing system 620 may include a global positioning system 621 (the global positioning system may be a GPS system, a beidou system or other positioning system), an Inertial Measurement Unit (IMU) 622, a laser radar 623, a millimeter wave radar 624, an ultrasonic radar 625, and a camera 626. The sensing system 620 may also include sensors of internal systems of the monitored vehicle 600 (e.g., an in-vehicle air quality monitor, a fuel gauge, an oil temperature gauge, etc.). Sensor data from one or more of these sensors may be used to detect the object and its corresponding characteristics (position, shape, orientation, velocity, etc.). Such detection and identification is a critical function of the safe operation of the vehicle 600.

Global positioning system 621 is used to estimate the geographic location of vehicle 600.

The inertial measurement unit 622 is used to sense a pose change of the vehicle 600 based on the inertial acceleration. In some embodiments, inertial measurement unit 622 may be a combination of accelerometers and gyroscopes.

Lidar 623 utilizes laser light to sense objects in the environment in which vehicle 600 is located. In some embodiments, lidar 623 may include one or more laser sources, laser scanners, and one or more detectors, among other system components.

The millimeter-wave radar 624 utilizes radio signals to sense objects within the surrounding environment of the vehicle 600. In some embodiments, in addition to sensing objects, the millimeter-wave radar 624 may also be used to sense the speed and/or heading of objects.

The ultrasonic radar 625 may sense objects around the vehicle 600 using ultrasonic signals.

The camera 626 is used to capture image information of the surroundings of the vehicle 600. The image capturing device 626 may include a monocular camera, a binocular camera, a structured light camera, a panoramic camera, and the like, and the image information acquired by the image capturing device 626 may include still images or video stream information.

Decision control system 630 includes a computing system 631 that makes analytical decisions based on information acquired by sensing system 620, decision control system 630 further includes a vehicle control unit 632 that controls the powertrain of vehicle 600, and a steering system 633, throttle 634, and brake system 635 for controlling vehicle 600.

The computing system 631 may operate to process and analyze the various information acquired by the perception system 620 to identify objects, and/or features in the environment surrounding the vehicle 600. The target may comprise a pedestrian or an animal and the objects and/or features may comprise traffic signals, road boundaries and obstacles. The computing system 631 may use object recognition algorithms, Structure From Motion (SFM) algorithms, video tracking, and the like. In some embodiments, the computing system 631 may be used to map an environment, track objects, estimate the speed of objects, and so forth. The computing system 631 may analyze the various information obtained and derive a control strategy for the vehicle.

The vehicle controller 632 may be used to perform coordinated control on the power battery and the engine 641 of the vehicle to improve the power performance of the vehicle 600.

The steering system 633 is operable to adjust the heading of the vehicle 600. For example, in one embodiment, a steering wheel system.

The throttle 634 is used to control the operating speed of the engine 641 and thus the speed of the vehicle 600.

The braking system 635 is used to control the deceleration of the vehicle 600. The braking system 635 may use friction to slow the wheel 644. In some embodiments, the braking system 635 may convert the kinetic energy of the wheels 644 into electrical current. The braking system 635 may also take other forms to slow the rotational speed of the wheels 644 to control the speed of the vehicle 600.

The drive system 640 may include components that provide powered motion to the vehicle 600. In one embodiment, the drive system 640 may include an engine 641, an energy source 642, a transmission 643, and wheels 644. The engine 641 may be an internal combustion engine, an electric motor, an air compression engine, or other types of engine combinations, such as a hybrid engine consisting of a gasoline engine and an electric motor, a hybrid engine consisting of an internal combustion engine and an air compression engine. The engine 641 converts the energy source 642 into mechanical energy.

Examples of energy sources 642 include gasoline, diesel, other petroleum-based fuels, propane, other compressed gas-based fuels, ethanol, solar panels, batteries, and other sources of electrical power. The energy source 642 may also provide energy to other systems of the vehicle 600.

The transmission 643 may transmit mechanical power from the engine 641 to the wheels 644. The transmission 643 may include a gearbox, a differential, and a drive shaft. In one embodiment, the transmission 643 may also include other components, such as clutches. Wherein the drive shaft may include one or more axles that may be coupled to one or more wheels 644.

Some or all of the functionality of the vehicle 600 is controlled by the computing platform 650. Computing platform 650 can include at least one processor 651, which processor 651 can execute instructions 653 stored in a non-transitory computer-readable medium, such as memory 652. In some embodiments, the computing platform 650 may also be a plurality of computing devices that control individual components or subsystems of the vehicle 600 in a distributed manner.

The processor 651 can be any conventional processor, such as a commercially available CPU. Alternatively, processor 651 may also comprise a processor such as a Graphics Processing Unit (GPU), Field Programmable Gate Array (FPGA), System On Chip (SOC), Application Specific Integrated Circuit (ASIC), or a combination thereof. Although fig. 7 functionally illustrates a processor, memory, and other elements of a computer in the same block, those skilled in the art will appreciate that the processor, computer, or memory may actually comprise multiple processors, computers, or memories that may or may not be stored within the same physical housing. For example, the memory may be a hard drive or other storage medium located in a different housing than the computer. Thus, references to a processor or computer are to be understood as including references to a collection of processors or computers or memories which may or may not operate in parallel. Rather than using a single processor to perform the steps described herein, some components, such as the steering component and the retarding component, may each have their own processor that performs only computations related to the component-specific functions.

In the disclosed embodiment, the processor 651 may perform the above-described fault tracking method.

In various aspects described herein, the processor 651 may be located remotely from the vehicle and in wireless communication with the vehicle. In other aspects, some of the processes described herein are executed on a processor disposed within the vehicle and others are executed by a remote processor, including taking the steps necessary to perform a single maneuver.

In some embodiments, the memory 652 may contain instructions 653 (e.g., program logic), which instructions 653 may be executed by the processor 651 to perform various functions of the vehicle 600. The memory 652 may also contain additional instructions, including instructions to send data to, receive data from, interact with, and/or control one or more of the infotainment system 610, the perception system 620, the decision control system 630, the drive system 640.

In addition to instructions 653, memory 652 may store data such as road maps, route information, the location, direction, speed of the vehicle, and other such vehicle data, as well as other information. Such information may be used by the vehicle 600 and the computing platform 650 during operation of the vehicle 600 in autonomous, semi-autonomous, and/or manual modes.

Computing platform 650 may control functions of vehicle 600 based on inputs received from various subsystems (e.g., drive system 640, perception system 620, and decision control system 630). For example, computing platform 650 may utilize input from decision control system 630 in order to control steering system 633 to avoid obstacles detected by perception system 620. In some embodiments, the computing platform 650 is operable to provide control over many aspects of the vehicle 600 and its subsystems.

Optionally, one or more of these components described above may be mounted separately from or associated with the vehicle 600. For example, the memory 652 may exist partially or completely separate from the vehicle 600. The above components may be communicatively coupled together in a wired and/or wireless manner.

Optionally, the above components are only an example, in an actual application, components in the above modules may be added or deleted according to an actual need, and fig. 7 should not be construed as limiting the embodiment of the present disclosure.

An autonomous automobile traveling on a roadway, such as vehicle 600 above, may identify objects within its surrounding environment to determine an adjustment to the current speed. The object may be another vehicle, a traffic control device, or another type of object. In some examples, each identified object may be considered independently, and based on the respective characteristics of the object, such as its current speed, acceleration, separation from the vehicle, etc., may be used to determine the speed at which the autonomous vehicle is to be adjusted.

Optionally, the vehicle 600 or a sensory and computing device associated with the vehicle 600 (e.g., computing system 631, computing platform 650) may predict behavior of the identified object based on characteristics of the identified object and the state of the surrounding environment (e.g., traffic, rain, ice on the road, etc.). Optionally, each identified object depends on the behavior of each other, so it is also possible to predict the behavior of a single identified object taking all identified objects together into account. The vehicle 600 is able to adjust its speed based on the predicted behavior of the identified object. In other words, the autonomous vehicle is able to determine what steady state the vehicle will need to adjust to (e.g., accelerate, decelerate, or stop) based on the predicted behavior of the object. In this process, other factors may also be considered to determine the speed of the vehicle 600, such as the lateral position of the vehicle 600 in the road being traveled, the curvature of the road, the proximity of static and dynamic objects, and so forth.

In addition to providing instructions to adjust the speed of the autonomous vehicle, the computing device may also provide instructions to modify the steering angle of the vehicle 600 to cause the autonomous vehicle to follow a given trajectory and/or maintain a safe lateral and longitudinal distance from objects in the vicinity of the autonomous vehicle (e.g., vehicles in adjacent lanes on the road).

The vehicle 600 may be any type of vehicle, such as a car, a truck, a motorcycle, a bus, a boat, an airplane, a helicopter, a recreational vehicle, a train, etc., and the disclosed embodiment is not particularly limited.

In another exemplary embodiment, a computer program product is also provided, which comprises a computer program executable by a programmable apparatus, the computer program having code portions for performing the above-mentioned fault tracking method when executed by the programmable apparatus.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

It will be understood that the present disclosure is not limited to the precise arrangements that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method of fault tracking, comprising:

acquiring source information of input information of the element parts related to automatic driving operation and execution duration of each operation of each element part;

tracking the fault of the automatic driving based on the source information and the execution duration.

2. The method according to claim 1, characterized in that the source information comprises which upstream meta-component the input information of the meta-component originates from and the input information is successfully generated by the upstream meta-component after a few executions.

3. The method of claim 2, wherein tracking the fault of autonomous driving based on the source information and the execution duration comprises: determining that a communication failure has occurred between the meta-component and the upstream meta-component in a case where the source information indicates that the meta-component has not received the input information from the upstream meta-component.

4. The method of claim 2, wherein tracking the fault of autonomous driving based on the source information and the execution duration comprises: and determining that the upstream element has a fault when the source information indicates that the execution times of the upstream element exceed a preset time.

5. The method of claim 1, wherein tracking the failure of autonomous driving based on the source information and the execution duration comprises: and if the execution time length of each operation of the element component exceeds the preset execution time length, determining that the element component has a fault.

6. The method of claim 1, further comprising:

and determining at least one of the average execution time length, the maximum execution time length, the minimum execution time length and the P90 time consumption of each meta-component for executing the operation according to the execution time length of each operation of each meta-component.

7. The method of claim 1, further comprising:

analyzing the source information and the execution duration into data in a set format; and

and outputting the data with the set format to a user.

8. A fault tracking device, comprising:

the system comprises an acquisition module, a judgment module and a display module, wherein the acquisition module is used for acquiring source information of input information of the element related to automatic driving operation and execution duration of each operation of each element;

and the tracking module is used for tracking the fault of the automatic driving based on the source information and the execution duration.

9. A fault tracking device, comprising:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to perform the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, on which computer program instructions are stored, which program instructions, when executed by a processor, carry out the steps of the method according to any one of claims 1 to 7.

11. A chip comprising a processor and an interface; the processor is configured to read instructions to perform the method of any of claims 1 to 7.