Fault tracing method, device, medium and chip

Abstract

This disclosure relates to a fault tracing method, device, medium, and chip in the field of vehicles, capable of effectively tracking autonomous driving faults. The fault tracing method includes obtaining source information of input information from components involved in autonomous driving operations and the execution duration of each operation of each component; and tracking autonomous driving faults based on the source information and execution duration.

Description

Technical Field

[0001] The present disclosure relates to the field of vehicles, and in particular to a fault tracing method, device, medium, and chip. Background Art

[0002] Autonomous driving requires the collaboration of numerous sensors and algorithms. Because these numerous sensors and algorithms are involved, it can be difficult to identify the root cause of any anomalies. Summary of the Invention

[0003] To overcome the problems existing in the related art, the present disclosure provides a fault tracing method, device, medium and chip.

[0004] According to a first aspect of an embodiment of the present disclosure, a fault tracing method is provided, comprising: obtaining source information of input information of components involved in autonomous driving operation and the execution duration of each operation of each component; and tracking the fault of autonomous driving based on the source information and the execution duration.

[0005] Optionally, the source information includes which upstream component the input information of the component originates from and the number of times the upstream component is executed before the input information is successfully generated.

[0006] Optionally, the tracking of autonomous driving faults based on the source information and the execution duration includes: determining that a communication failure has occurred between the component and the upstream component when the source information indicates that the component has not received the input information from the upstream component.

[0007] Optionally, the tracking of autonomous driving faults based on the source information and the execution duration includes: when the source information indicates that the execution times of the upstream component exceed a preset number, determining that the upstream component has a fault.

[0008] Optionally, the tracking of autonomous driving faults based on the source information and the execution time includes: if the execution time of each operation of the component exceeds the preset execution time, determining that the component has failed.

[0009] Optionally, the method further includes: determining at least one of an average execution time, a maximum execution time, a minimum execution time and a P90 time of the operation executed by each component according to the execution time of each operation of each component.

[0010] Optionally, the method further includes: parsing the source information and the execution duration into data in a set format; and outputting the data in the set format to a user.

[0011] According to the second aspect of an embodiment of the present disclosure, a fault tracking device is provided, including: an acquisition module for obtaining source information of input information of components involved in autonomous driving operation and the execution duration of each operation of each component; and a tracking module for tracking autonomous driving faults based on the source information and the execution duration.

[0012] According to a third aspect of an embodiment of the present disclosure, a fault tracing device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the steps of the method according to any one of the first aspect of the present disclosure.

[0013] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which computer program instructions are stored. When the program instructions are executed by a processor, the steps of the method according to any one of the first aspects of the present disclosure are implemented.

[0014] According to a fifth aspect of an embodiment of the present disclosure, a chip is provided, comprising a processor and an interface; the processor is configured to read instructions to execute any one of the methods described in the first aspect of the present disclosure.

[0015] By adopting the above technical solution, since it is possible to obtain the source information of the input information of the components involved in the operation of autonomous driving and the execution time of each operation of each component, and to track the autonomous driving fault based on the source information and execution time, that is, through the source information and execution time, the components can be accurately located, and the execution status of each module involved in autonomous driving can be effectively analyzed and checked, so as to accurately determine in which component the autonomous driving fault occurred.

[0016] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] 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 present disclosure.

[0018] Figure 1 The figure is a flowchart of a fault tracing method according to an exemplary embodiment.

[0019] Figure 2 is another flow chart showing a fault tracing method according to an exemplary embodiment.

[0020] Figure 3 is another flow chart showing a fault tracing method according to an exemplary embodiment.

[0021] Figure 4 The figure is a block diagram of a fault tracing device according to an exemplary embodiment.

[0022] Figure 5 This is another block diagram of a fault tracing device according to an exemplary embodiment.

[0023] Figure 6 This is another block diagram of a fault tracing device according to an exemplary embodiment.

[0024] Figure 7 It is a functional block diagram of a vehicle shown in an exemplary embodiment. DETAILED DESCRIPTION

[0025] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments 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.

[0026] It should be noted that all actions of acquiring signals, information or data in this application are carried out in compliance with the relevant data protection laws and policies of the country where they are located and with the authorization given by the owner of the corresponding device.

[0027] Figure 1 FIG. 1 is a flow chart showing a fault tracing method according to an exemplary embodiment. The fault tracing method can be applied to an autonomous driving vehicle. Figure 1 As shown, the fault tracing method includes the following steps S11 and S12.

[0028] In step S11, the source information of the input information of the components involved in the automatic driving operation and the execution time of each operation of each component are obtained.

[0029] Autonomous driving involves numerous sensors and algorithm modules. These sensors and algorithm modules form a directed graph based on data dependencies and data flows. Specifically, each sensor and algorithm module is a node in the directed graph, and the data from output to input is a directed edge in the directed graph.

[0030] For example, during autonomous driving, a camera detects a person ahead and transmits the detection result to a path planning algorithm. The path planning algorithm then plans a path based on the camera's detection result. The subsequent driving algorithm then controls the vehicle's autonomous driving based on the path planning algorithm's results. The camera, path planning algorithm, and driving algorithm are nodes in a directed graph. The directed edges of the graph are formed from the camera's output to the path planning algorithm's input, and so on.

[0031] In some embodiments, the source information may include which upstream component the component's input information originates from and how many times the upstream component executes before the input information is successfully generated. Still taking the previous camera example, for the path planning algorithm, the source information of the path planning algorithm includes whether the input information of the path planning algorithm (that is, the image of a person in front of the camera captured by the camera) comes from the camera, and how many times the camera executes the video capture to successfully capture the input information of the path planning algorithm (that is, the image of a person in front of the camera captured by the camera). For example, during the video capture period, the camera executes the Nth video capture operation before successfully capturing the image, where N is greater than or equal to 1. That is to say, in the first N-1 video capture operations, although the camera executed the video capture operation, it did not successfully capture the image.

[0032] In some embodiments, the execution time of each operation of each component refers to the time it takes for each component to perform the operation it is to perform once. Still taking the camera example mentioned above, for the camera, its execution time refers to the time it takes for the camera to perform one video capture.

[0033] In step S12, the autonomous driving fault is tracked based on the source information and execution duration.

[0034] In some embodiments, if the source information indicates that the component has not received input information from the upstream component, it is determined that a communication failure has occurred between the component and the upstream component. Still using the camera example described above, if the source information indicates that the path planning module has not received any images from the camera, this indicates that a communication failure has occurred between the camera and the path planning module.

[0035] In some embodiments, if the source information indicates that the upstream component has executed more times than a preset number of times, a fault is determined in the upstream component. Still using the camera example described above, assuming the source information indicates that the input information received by the path planning algorithm was successfully captured by the camera after executing five camera operations (i.e., in the first four camera operations, although the camera executed the camera operation, it did not successfully capture an image), and the preset fault-tolerant execution number is three times (i.e., if the camera executes three consecutive camera operations without capturing an image), then since the camera executed more than the preset number of times when it successfully captured an image, it can be determined that the camera has failed.

[0036] In some embodiments, if the execution time of each operation of a component exceeds a preset execution time, the component is determined to have failed. Still using the camera example described above, assuming that the camera is set to perform 10 camera operations per second and each camera operation lasts 100ms, then if the execution time of a camera operation is 150ms, the camera is considered to have failed because the execution time of this camera operation exceeds the 100ms requirement, which will affect the next operation.

[0037] By adopting the above technical solution, since it is possible to obtain the source information of the input information of the components involved in the operation of autonomous driving and the execution time of each operation of each component, and to track the autonomous driving fault based on the source information and execution time, that is, through the source information and execution time, the components can be accurately located, and the execution status of each module involved in autonomous driving can be effectively analyzed and checked, so as to accurately determine in which component the autonomous driving fault occurred.

[0038] Figure 2 FIG. 1 is another flow chart showing a fault tracing method according to an exemplary embodiment. Figure 2 As shown, in Figure 1 On the basis of, the fault tracing method further includes step S13: determining at least one of the average execution time, maximum execution time, minimum execution time and P90 time of each component operation according to the execution time of each operation of each component.

[0039] Still taking the camera example described above as an example, based on the execution time of each camera operation within a period of time, the average execution time, maximum execution time, minimum execution time and P90 time of the camera within the period can be determined.

[0040] By adopting the above technical solution, since it is possible to determine at least one of the average execution time, maximum execution time, minimum execution time and P90 time of each component's execution operation based on the execution time of each operation of each component, it is possible to use information such as average execution time, maximum execution time, minimum execution time and P90 time to evaluate the performance of each component, which is conducive to accurately analyzing the performance of each component involved in autonomous driving, and thus is conducive to improving autonomous driving.

[0041] Figure 3 FIG. 1 is another flow chart showing a fault tracing method according to an exemplary embodiment. Figure 3 As shown, in Figure 1 On this basis, the fault tracing method further includes steps S14 and S15.

[0042] In step S14, the source information and execution duration are parsed into data in a set format.

[0043] The format of the parsed data can be set according to the actual scenario. For example, the data can be parsed into an XML format file, a PDF format file, etc.

[0044] In step S15 , the data in the set format is output to the user.

[0045] For example, if the file is in XML format, the data can be loaded through a web page.

[0046] By adopting the above technical solution, since the source information and execution time can be parsed into data in a set format and output to the user, the user can understand the execution status of each component of the autonomous driving, and thus analyze and troubleshoot the source of the autonomous driving fault.

[0047] Figure 4 FIG. 1 is a block diagram of a fault tracing device according to an exemplary embodiment. The fault tracing device can be applied to an autonomous vehicle. Figure 4 As shown, the fault tracking device includes: an acquisition module 41, which is used to obtain source information of input information of components involved in autonomous driving operation and the execution time of each operation of each component; a tracking module 42, which is used to track autonomous driving faults based on the source information and execution time.

[0048] By adopting the above technical solution, since it is possible to obtain the source information of the input information of the components involved in the operation of autonomous driving and the execution time of each operation of each component, and to track the autonomous driving fault based on the source information and execution time, that is, through the source information and execution time, the components can be accurately located, and the execution status of each module involved in autonomous driving can be effectively analyzed and checked, so as to accurately determine in which component the autonomous driving fault occurred.

[0049] Optionally, the source information includes which upstream component the input information of the component originates from and the number of times the upstream component is executed before the input information is successfully generated.

[0050] Optionally, when the source information indicates that the component has not received the input information from the upstream component, the tracking module 42 determines that a communication failure has occurred between the component and the upstream component.

[0051] Optionally, when the source information indicates that the execution times of the upstream component exceed a preset number, the tracking module 42 determines that the upstream component has a fault.

[0052] Optionally, if the execution time of each operation of the component exceeds a preset execution time, the tracking module 42 determines that the component has failed.

[0053] Figure 5 This is another block diagram of a fault tracing device according to an exemplary embodiment. The fault tracing device further includes a determination module 43 configured to determine at least one of an average execution time, a maximum execution time, a minimum execution time, and a P90 time for each component operation based on the execution time of each operation of each component.

[0054] Figure 6 FIG. 1 is another block diagram of a fault tracing device according to an exemplary embodiment. Figure 6 As shown, the fault tracing device further includes: a parsing module 44 for parsing the source information and the execution duration into data in a set format; and an output module 45 for outputting the data in the set format to a user.

[0055] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0056] The present disclosure also provides a fault tracing device, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the steps of the method according to the present disclosure.

[0057] The present disclosure also provides a computer-readable storage medium having computer program instructions stored thereon, wherein the program instructions implement the steps of the method according to the present disclosure when executed by a processor.

[0058] The present disclosure also provides a chip, characterized in that it includes a processor and an interface; the processor is used to read instructions to execute the steps of the method described in the present disclosure. The chip can be an integrated circuit (IC). The chip can include but is not limited to the following types: GPU (Graphics Processing Unit), CPU (Central Processing Unit), FPGA (Field Programmable Gate Array), DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), SOC (System on Chip, SoC, system on chip or system-on-chip), etc. The above-mentioned integrated circuit or chip can be used to execute executable instructions (or codes) to implement the above-mentioned fault tracing method. The executable instructions can be stored in the integrated circuit or chip, or can be obtained from other devices or equipment. For example, the integrated circuit or chip includes a processor, a memory, and an interface for communicating with other devices. The executable instruction can be stored in the memory, and when the executable instruction is executed by the processor, the above-mentioned fault tracing method is implemented; alternatively, the integrated circuit or chip can receive the executable instruction through the interface and transmit it to the processor for execution to implement the above-mentioned fault tracing method.

[0059] See Figure 7 , Figure 7 FIG6 is a functional block diagram of a vehicle 600, illustrating an exemplary embodiment. Vehicle 600 can be configured for fully or partially autonomous driving. For example, vehicle 600 can obtain environmental information about its surroundings through perception system 620 and, based on analysis of the environmental information, derive an autonomous driving strategy to achieve fully autonomous driving, or present the analysis results to the user to achieve partially autonomous driving.

[0060] Vehicle 600 may include various subsystems, such as an infotainment system 610, a perception system 620, a decision control system 630, a drive system 640, and a computing platform 650. Alternatively, vehicle 600 may include more or fewer subsystems, and each subsystem may include multiple components. Furthermore, each subsystem and component of vehicle 600 may be interconnected via wired or wireless means.

[0061] In some embodiments, infotainment system 610 may include a communication system 611 , an entertainment system 612 , and a navigation system 613 .

[0062] The communication system 611 may include a wireless communication system that can communicate wirelessly with one or more devices directly or via a communication network. For example, the wireless communication system can use 3G cellular communication, such as CDMA, EVDO, GSM / GPRS, or 4G cellular communication, such as LTE. Or 5G cellular communication. The wireless communication system can use WiFi to communicate with a wireless local area network (WLAN). In some embodiments, the wireless communication system can use an infrared link, Bluetooth, or ZigBee to communicate directly with the device. Other wireless protocols, such as various vehicle communication systems, for example, the wireless communication system may include one or more dedicated short range communications (DSRC) devices, which may include public and / or private data communications between vehicles and / or roadside stations.

[0063] The entertainment system 612 may include a display device, a microphone and speakers. Users can listen to the radio and play music in the car based on the entertainment system; or connect the mobile phone to the vehicle and project the mobile phone screen on the display device. The display device can be touch-sensitive and the user can operate it by touching the screen.

[0064] In some cases, the user's voice signal can be obtained through a microphone, and based on the analysis of the user's voice signal, the user can control certain aspects of the vehicle 600, such as adjusting the temperature inside the vehicle, etc. In other cases, music can be played to the user through a speaker.

[0065] The navigation system 613 may include a map service provided by a map provider, thereby providing navigation for the vehicle 600. The navigation system 613 may be used in conjunction with the vehicle's global positioning system 621 and inertial measurement unit 622. The map service provided by the map provider may be a two-dimensional map or a high-precision map.

[0066] The perception system 620 may include several sensors that sense information about the environment surrounding the vehicle 600. For example, the perception system 620 may include a global positioning system 621 (the global positioning system may be a GPS system, or a BeiDou system or other positioning system), an inertial measurement unit (IMU) 622, a lidar 623, a millimeter wave radar 624, an ultrasonic radar 625, and a camera 626. The perception system 620 may also include sensors of the internal systems of the monitored vehicle 600 (for example, an in-vehicle air quality monitor, a fuel gauge, an oil temperature gauge, etc.). Sensor data from one or more of these sensors can be used to detect objects and their corresponding characteristics (position, shape, direction, speed, etc.). Such detection and recognition are key functions for the safe operation of the vehicle 600.

[0067] The global positioning system 621 is used to estimate the geographic location of the vehicle 600 .

[0068] The inertial measurement unit 622 is used to sense the posture change of the vehicle 600 based on inertial acceleration. In some embodiments, the inertial measurement unit 622 can be a combination of an accelerometer and a gyroscope.

[0069] 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, a laser scanner, and one or more detectors, among other system components.

[0070] The millimeter wave radar 624 uses radio signals to sense objects in the surrounding environment of the vehicle 600. In some embodiments, in addition to sensing objects, the millimeter wave radar 624 can also be used to sense the speed and / or heading of the objects.

[0071] The ultrasonic radar 625 may sense objects around the vehicle 600 using ultrasonic signals.

[0072] The camera device 626 is used to capture image information of the surrounding environment of the vehicle 600. The camera device 626 may include a monocular camera, a binocular camera, a structured light camera, a panoramic camera, etc. The image information obtained by the camera device 626 may include static images or video stream information.

[0073] The decision control system 630 includes a computing system 631 that analyzes and makes decisions based on the information obtained by the perception system 620. The decision control system 630 also includes a vehicle controller 632 that controls the power system of the vehicle 600, as well as a steering system 633, throttle 634 and braking system 635 for controlling the vehicle 600.

[0074] The computing system 631 can be operated to process and analyze various information obtained by the perception system 620 in order to identify targets, objects and / or features in the environment surrounding the vehicle 600. Targets may include pedestrians or animals, and objects and / or features may include traffic signals, road boundaries and obstacles. The computing system 631 may use object recognition algorithms, Structure from Motion (SFM) algorithms, video tracking and other technologies. In some embodiments, the computing system 631 can be used to map the environment, track objects, estimate the speed of objects, and so on. The computing system 631 can analyze the various information obtained and derive a control strategy for the vehicle.

[0075] The vehicle controller 632 can be used to coordinate and control the vehicle's power battery and engine 641 to improve the power performance of the vehicle 600.

[0076] The steering system 633 is operable to adjust the forward direction of the vehicle 600. For example, in one embodiment, it may be a steering wheel system.

[0077] The throttle 634 is used to control the operating speed of the engine 641 and thereby control the speed of the vehicle 600 .

[0078] Braking system 635 is used to control the deceleration of vehicle 600. Braking system 635 can use friction to slow down wheels 644. In some embodiments, braking system 635 can convert the kinetic energy of wheels 644 into electric current. Braking system 635 can also take other forms to slow the rotation speed of wheels 644 and thus control the speed of vehicle 600.

[0079] Drive system 640 may include components that provide powered motion for vehicle 600. In one embodiment, drive system 640 may include an engine 641, an energy source 642, a transmission system 643, and wheels 644. Engine 641 may be an internal combustion engine, an electric motor, an air compression engine, or another combination of engines, such as a hybrid engine consisting of a gasoline engine and an electric motor, or a hybrid engine consisting of an internal combustion engine and an air compression engine. Engine 641 converts energy source 642 into mechanical energy.

[0080] Examples of energy source 642 include gasoline, diesel, other petroleum-based fuels, propane, other compressed gas-based fuels, ethanol, solar panels, batteries, and other sources of electricity. Energy source 642 can also provide energy to other systems of vehicle 600.

[0081] The transmission system 643 can transmit mechanical power from the engine 641 to the wheels 644. The transmission system 643 may include a gearbox, a differential, and a drive shaft. In one embodiment, the transmission system 643 may also include other components, such as a clutch. The drive shaft may include one or more shafts that can be coupled to one or more wheels 644.

[0082] Some or all functions of vehicle 600 are controlled by a computing platform 650. Computing platform 650 may include at least one processor 651 that can execute instructions 653 stored in a non-transitory computer-readable medium such as memory 652. In some embodiments, computing platform 650 may also be a plurality of computing devices that control individual components or subsystems of vehicle 600 in a distributed manner.

[0083] The processor 651 may be any conventional processor, such as a commercially available CPU. Alternatively, the processor 651 may include a graphics processor (GPU), a field programmable gate array (FPGA), a system on chip (SOC), an application specific integrated circuit (ASIC), or a combination thereof. Figure 7 Functionally, processor, memory, and other elements of the computer in the same block are illustrated, but those of ordinary skill in the art will appreciate that the processor, computer, or memory may in fact comprise a plurality of processors, computers, or memories that may or may not be stored in the same physical housing. For example, the memory may be a hard drive or other storage medium that is positioned in a housing that is different from the computer. Therefore, reference to a processor or computer will be understood to include reference to a collection of processors or computers or memories that may or may not operate in parallel. Different from using a single processor to perform the steps described herein, some assemblies such as steering assembly and deceleration assembly may each have their own processor that only performs the calculations relevant to the functions specific to the assembly.

[0084] In the embodiment of the present disclosure, the processor 651 may execute the above-mentioned fault tracing method.

[0085] In various aspects described herein, the processor 651 can be located remotely from the vehicle and in wireless communication with the vehicle. In other aspects, some of the processes described herein are performed on a processor disposed within the vehicle while others are performed by a remote processor, including taking the necessary steps to perform a single maneuver.

[0086] In some embodiments, the memory 652 may include instructions 653 (e.g., program logic) that are executable by the processor 651 to perform various functions of the vehicle 600. The memory 652 may also include additional instructions, including instructions for sending data to, receiving data from, interacting with, and / or controlling one or more of the infotainment system 610, the perception system 620, the decision control system 630, and the drive system 640.

[0087] In addition to instructions 653, memory 652 may also store data such as road maps, route information, the vehicle's location, direction, speed, and other such vehicle data, as well as other information. This information may be used by vehicle 600 and computing platform 650 during operation of vehicle 600 in autonomous, semi-autonomous, and / or manual modes.

[0088] The computing platform 650 may control functions of the vehicle 600 based on input received from various subsystems, such as the drive system 640, the perception system 620, and the decision control system 630. For example, the computing platform 650 may utilize input from the decision control system 630 to control the steering system 633 to avoid an obstacle detected by the perception system 620. In some embodiments, the computing platform 650 may be operable to provide control over many aspects of the vehicle 600 and its subsystems.

[0089] Alternatively, one or more of the above components may be installed or associated separately from the vehicle 600. For example, the memory 652 may be partially or completely separate from the vehicle 600. The above components may be communicatively coupled together in a wired and / or wireless manner.

[0090] Optionally, the above components are just an example. In actual applications, the components in the above modules may be added or deleted according to actual needs. Figure 7 It should not be understood as limiting the embodiments of the present disclosure.

[0091] An autonomous vehicle traveling on a road, such as vehicle 600 above, can identify objects in its surroundings to determine adjustments to its current speed. Objects can be other vehicles, traffic control devices, or other types of objects. In some examples, each identified object can be considered independently, and the speed adjustment to be made to the autonomous vehicle can be determined based on its respective characteristics, such as its current speed, acceleration, and distance from the vehicle.

[0092] Optionally, the vehicle 600 or a sensing and computing device associated with the vehicle 600 (e.g., computing system 631, computing platform 650) can predict the behavior of the identified objects based on the characteristics of the identified objects 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 the behavior of all identified objects can also be considered together to predict the behavior of a single identified object. The vehicle 600 can adjust its speed based on the predicted behavior of the identified objects. In other words, the autonomous vehicle can determine what stable state the vehicle will need to adjust to (e.g., accelerate, decelerate, or stop) based on the predicted behavior of the objects. In this process, other factors can also be considered to determine the speed of the vehicle 600, such as the lateral position of the vehicle 600 in the road it is traveling on, the curvature of the road, the proximity of static and dynamic objects, etc.

[0093] 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 vehicle 600 so that the autonomous vehicle follows a given trajectory and / or maintains a safe lateral and longitudinal distance from objects near the autonomous vehicle (e.g., vehicles in adjacent lanes on the road).

[0094] The vehicle 600 may be any type of vehicle, such as a car, a truck, a motorcycle, a bus, a ship, an airplane, a helicopter, an RV, a train, etc., and the present disclosure does not impose any particular limitation thereto.

[0095] In another exemplary embodiment, a computer program product is further provided. The computer program product includes a computer program executable by a programmable device, and the computer program has a code portion for executing the above-mentioned fault tracing method when executed by the programmable device.

[0096] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the present disclosure. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the following claims.

[0097] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A fault tracing method, characterized in that: include: Obtaining source information of input information of components involved in autonomous driving operations and the execution duration of each operation of each component, the source information including which upstream component the input information of the component originates from and the number of executions of the upstream component before the input information is successfully generated; Based on the source information and the execution duration, autonomous driving faults are tracked.

2. The method according to claim 1, characterized in that The tracking of autonomous driving faults based on the source information and the execution duration includes: determining that a communication failure has occurred between the component and the upstream component when the source information indicates that the component has not received the input information from the upstream component.

3. The method according to claim 1, characterized in that The tracking of the autonomous driving fault based on the source information and the execution duration includes: when the source information indicates that the execution times of the upstream component exceed a preset number, determining that the upstream component has a fault.

4. The method according to claim 1, wherein The tracking of autonomous driving faults based on the source information and the execution time includes: if the execution time of each operation of the component exceeds the preset execution time, determining that the component has failed.

5. The method according to claim 1, wherein The method further comprises: At least one of an average execution time, a maximum execution time, a minimum execution time, and a P90 time of the operation performed by each component is determined according to the execution time of each operation of each component.

6. The method according to claim 1, wherein The method further comprises: Parsing the source information and the execution duration into data in a set format; and The data in the set format is output to the user.

7. A fault tracing device, characterized in that: include: an acquisition module, configured to obtain source information of input information of components involved in autonomous driving operation and the execution duration of each operation of each component, wherein the source information includes the upstream component from which the input information of the component originates and the number of executions of the upstream component before the input information is successfully generated; A tracking module is used to track the fault of autonomous driving based on the source information and the execution duration.

8. A fault tracing device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to execute the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the program instructions are executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A chip, characterized in that: The method comprises a processor and an interface; the processor is used to read instructions to execute the method according to any one of claims 1 to 6.

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