Self-checking method, device, system and electronic equipment of autonomous vehicle

Abstract

This application discloses a self-testing method, device, system, and electronic device for autonomous vehicles. The method is executed by the vehicle and includes: acquiring a connection status detection task for the autonomous vehicle; acquiring connection status detection results based on the connection status detection task, specifically including detection results of the three-terminal connection statuses: the connection between the vehicle-side application and the vehicle-side industrial control computer, the connection between the vehicle-side application and the cloud, and the connection between the vehicle-side industrial control computer and the cloud; acquiring the corresponding node status detection task if the connection status detection result indicates a normal connection status; determining the node status detection result of the autonomous vehicle based on the node status detection task; and determining the self-testing result of the autonomous vehicle based on the connection status detection result and the node status detection result. Based on the specific characteristics of autonomous driving scenarios, this application efficiently detects the three-terminal connection statuses and the status of each node, saving troubleshooting time and meeting the requirements of efficiency, safety, and reliability for autonomous driving scenarios.

Description

Technical Field

[0001] This application relates to the field of autonomous driving technology, and in particular to a self-testing method, device, system and electronic equipment for autonomous vehicles. Background Technology

[0002] While developing business requirements, developers of autonomous driving functions also need to ensure the stability of delivered projects and the timeliness of problem solving. In the current environment where autonomous vehicles do not have self-testing modules, developers need to sit in autonomous vehicles for a long time, prepare multiple debugging devices such as computer data cables, and because autonomous driving sections require testing at various speeds and in various environments, it is impossible to guarantee a stable driving environment. Therefore, in a relatively unstable driving environment, developers must consider their own safety while checking problem logs in a timely manner, which is a very time-consuming and relatively unsafe situation.

[0003] Maintaining reliable connectivity is crucial for the safe and stable operation of autonomous vehicles, as well as their ability to support business operations during driving. This connectivity includes the connections between the vehicle-side application and the vehicle's industrial control computer, between the vehicle-side application and the cloud, and between the vehicle's industrial control computer and the cloud. An anomaly in any data transmission link can significantly impact the autonomous vehicle's operation. However, current technology lacks an efficient solution for detecting the connectivity status of autonomous vehicles. If a data transmission link malfunctions during operation, technicians often need to follow the vehicle to investigate, reproduce, locate, and resolve the issue—a time-consuming and labor-intensive process.

[0004] Furthermore, if the autonomous driving function of an autonomous vehicle suddenly malfunctions during operation, the driver or other non-technical personnel cannot effectively troubleshoot the error in a timely manner. It requires the presence of relevant technical personnel from the malfunctioning vehicle to conduct complex reproduction process communication and code log tracking. Especially in multi-vehicle collaboration, the number of technical personnel is limited, making it impossible to track problems in a timely and effective manner. This leads to the current autonomous vehicle testing solutions being inefficient and costly. Summary of the Invention

[0005] This application provides a self-testing method, device, system, and electronic device for autonomous vehicles to improve the self-testing efficiency of autonomous vehicles and save troubleshooting time.

[0006] The embodiments of this application adopt the following technical solutions:

[0007] In a first aspect, embodiments of this application provide a self-testing method for an autonomous vehicle, wherein the method is executed by the vehicle itself, and the method includes:

[0008] Task: Obtain connectivity status detection for autonomous vehicles;

[0009] Based on the connection status detection task, the connection status detection results of the autonomous vehicle are obtained. The connection status detection results include the connection status detection results between the vehicle application and the vehicle industrial control computer, the connection status detection results between the vehicle application and the cloud, and the connection status detection results between the vehicle industrial control computer and the cloud.

[0010] If the connection status detection result is a normal connection status, obtain the corresponding node status detection task;

[0011] Based on the node state detection task, determine the node state detection result of the autonomous vehicle;

[0012] The self-test result of the autonomous vehicle is determined based on the connection status detection result and the node status detection result of the autonomous vehicle.

[0013] Optionally, obtaining the connection state detection result of the autonomous vehicle according to the connection state detection task includes:

[0014] Based on the connection state detection task, the connection state of the autonomous vehicle is detected, and the connection state detection result is obtained; and / or,

[0015] According to the connection status detection task, the connection status detection results of the autonomous vehicle are received from the cloud.

[0016] Optionally, after obtaining the connection state detection results of the autonomous vehicle according to the connection state detection task, the method further includes:

[0017] If the connection status detection result is a normal connection status, the first data is transmitted between the vehicle application and the vehicle industrial control computer through the connection between the vehicle application and the vehicle industrial control computer.

[0018] If the connection status detection result is a normal connection status, the second data is transmitted between the vehicle application and the cloud through the connection between the vehicle application and the cloud.

[0019] If the connection status detection result is a normal connection status, third data is transmitted between the vehicle-mounted industrial control computer and the cloud through the connection between the vehicle-mounted industrial control computer and the cloud.

[0020] Optionally, when the connection status detection result is a normal connection status, obtaining the corresponding node status detection task includes:

[0021] If the connection status detection result between the vehicle-side application and the vehicle-side industrial control computer is normal, the corresponding node status detection task is obtained.

[0022] Optionally, the task of obtaining the corresponding node state detection includes:

[0023] Determine the driving status of the autonomous vehicle;

[0024] When the autonomous vehicle is in autonomous driving mode, a first node state detection task is obtained, and the first node state detection task includes a first node state detection frequency.

[0025] When the driving state of the autonomous vehicle is in a non-autonomous driving state, a second node state detection task is obtained, and the second node state detection task includes a second node state detection frequency.

[0026] The first node status detection frequency is greater than the second node status detection frequency.

[0027] Optionally, determining the self-test result of the autonomous vehicle based on the connection status detection result and the node status detection result of the autonomous vehicle includes:

[0028] According to the first preset display strategy, the connection status detection result is displayed on the vehicle end. The first preset display strategy includes description information of connection type and connection status detection result.

[0029] According to the second preset display strategy, the node status detection results are displayed on the vehicle end. The second preset display strategy includes node type, node name, and descriptive information of the node status detection results.

[0030] Optionally, the node state detection results include abnormal nodes. After determining the node state detection results of the autonomous vehicle based on the node state detection task, the method further includes:

[0031] Determine the node type of the abnormal node;

[0032] Based on the node type of the abnormal node, a corresponding fault handling strategy is determined, and the abnormal node is handled through the fault handling strategy.

[0033] Optionally, after determining the self-test result of the autonomous vehicle based on the connection state detection result and the node state detection result of the autonomous vehicle, the method further includes:

[0034] The task is to obtain and report the self-inspection results of the autonomous vehicle.

[0035] Based on the self-inspection results, the task is to report the self-inspection results of the autonomous vehicle to the cloud.

[0036] Secondly, embodiments of this application also provide a self-testing device for an autonomous vehicle, wherein the device is applied to the vehicle end and includes:

[0037] The first acquisition unit is used to acquire the connection status detection task of the autonomous vehicle.

[0038] A connection status detection unit is used to obtain the connection status detection results of the autonomous vehicle according to the connection status detection task. The connection status detection results include the connection status detection results between the vehicle application and the vehicle industrial control computer, the connection status detection results between the vehicle application and the cloud, and the connection status detection results between the vehicle industrial control computer and the cloud.

[0039] The second acquisition unit is used to acquire the corresponding node status detection task when the connection status detection result is a normal connection status.

[0040] A node state detection unit is used to determine the node state detection result of the autonomous vehicle based on the node state detection task.

[0041] The first determining unit is used to determine the self-test result of the autonomous vehicle based on the connection status detection result and the node status detection result of the autonomous vehicle.

[0042] Thirdly, embodiments of this application also provide a self-testing system for autonomous vehicles, wherein the system includes a vehicle-side terminal and a cloud-side terminal, the vehicle-side terminal includes a vehicle-side application and a vehicle-side industrial control computer, and the vehicle-side terminal is used to execute any of the methods described above.

[0043] Fourthly, embodiments of this application also provide an electronic device, including:

[0044] Processor; and

[0045] A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform any of the methods described above.

[0046] Fifthly, embodiments of this application also provide a computer-readable storage medium that stores one or more programs, which, when executed by an electronic device including multiple applications, cause the electronic device to perform any of the methods described above.

[0047] The above-mentioned technical solutions adopted in the embodiments of this application can achieve the following beneficial effects: The self-testing method of the autonomous vehicle in the embodiments of this application is executed by the vehicle end. First, the connection status detection task of the autonomous vehicle is obtained; according to the connection status detection task, the connection status detection result of the autonomous vehicle is obtained, including the connection status detection result between the vehicle application and the vehicle industrial control computer, the connection status detection result between the vehicle application and the cloud, and the connection status detection result between the vehicle industrial control computer and the cloud; if the connection status detection result is a normal connection status, the corresponding node status detection task is obtained; according to the node status detection task, the node status detection result of the autonomous vehicle is determined; according to the connection status detection result and the node status detection result of the autonomous vehicle, the self-testing result of the autonomous vehicle is determined. The self-testing method of the autonomous vehicle in the embodiments of this application, based on the particularity of the autonomous driving scenario, efficiently detects the connection status of the three ends of the vehicle application-industrial control computer-cloud and the status of each node in the autonomous vehicle, saving troubleshooting time and meeting the requirements of the autonomous driving scenario for efficiency, safety and reliability. Attached Figure Description

[0048] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0049] Figure 1 This is a flowchart illustrating a self-testing method for an autonomous vehicle according to an embodiment of this application.

[0050] Figure 2 This is a schematic diagram of the display interface for the connection status detection result of an autonomous vehicle in an embodiment of this application;

[0051] Figure 3 This is a schematic diagram of the display interface of the node status detection result of a hardware node in an embodiment of this application;

[0052] Figure 4 This is a schematic diagram of the display interface for the node status detection result of a software node in an embodiment of this application.

[0053] Figure 5 This is a schematic diagram of the structure of a self-testing device for an autonomous vehicle according to an embodiment of this application;

[0054] Figure 6 This is a schematic diagram of the architecture of a self-testing system for an autonomous vehicle according to an embodiment of this application;

[0055] Figure 7 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0057] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0058] This application provides a self-testing method for autonomous vehicles, such as... Figure 1 The diagram shows a flowchart of a self-testing method for an autonomous vehicle according to an embodiment of this application. The method is executed by the vehicle and includes at least the following steps S110 to S140:

[0059] Step S110: Obtain the connection status detection task for the autonomous vehicle.

[0060] The self-testing method for autonomous vehicles in this application embodiment can be executed by the vehicle-side, i.e., the autonomous vehicle. The vehicle-side in this application embodiment mainly includes two parts: vehicle-side application (such as Eagle Eye APP) and vehicle-side industrial control computer. The main role of the vehicle-side application is to receive and render basic data and present cloud-based early warning capabilities. The vehicle-side industrial control computer can be regarded as the core system of the vehicle-side, which is responsible for supporting some business capabilities of the vehicle-side application and acting as an intermediary for transmitting some core data.

[0061] Since the connectivity status of autonomous vehicles is the foundation for realizing most autonomous driving functions, this embodiment of the application needs to obtain the connectivity status detection task of autonomous vehicles before performing self-tests. This connectivity status detection task can be created and executed by the vehicle-side application. The specific detection frequency of the connectivity status detection task can be flexibly set according to the actual business scenario and actual needs, and is not specifically limited here.

[0062] Step S120: Based on the connection status detection task, obtain the connection status detection results of the autonomous vehicle. The connection status detection results include the connection status detection results between the vehicle application and the vehicle industrial control computer, the connection status detection results between the vehicle application and the cloud, and the connection status detection results between the vehicle industrial control computer and the cloud.

[0063] The connection status of the autonomous vehicle detected in this application embodiment mainly includes the connection status between the vehicle-side application and the vehicle-side industrial control computer, the connection status between the vehicle-side application and the cloud, and the connection status between the vehicle-side industrial control computer and the cloud, namely, the three-terminal connection status.

[0064] Specifically, the vehicle-side application and the vehicle-side industrial control computer are constantly interconnected. This interconnection is primarily used to transmit and receive perception and positioning data from the vehicle-side application, as well as to support the reverse control of business scenarios, such as triggering and terminating autonomous driving. Therefore, the connection between the vehicle-side application and the vehicle-side industrial control computer ensures the support of basic data, such as the classification of roadside objects, which is frequently rendered through the vehicle-side application, or pedestrian warnings; high-precision positioning information is also frequently transmitted back to the vehicle-side application and uploaded to the cloud via the vehicle-side application's positioning APK to receive cloud-based business warnings. Almost all business capabilities and the accurate rendering of high-precision maps depend on a stable connection between the two ends.

[0065] The interconnection between vehicle-side applications and the cloud (such as AI cloud) mainly involves supporting business-layer capabilities. This includes supporting V2X warning events such as real-time roadside alerts from the cloud, demonstration capabilities such as issuing traffic police tasks, and supporting the presentation of traffic light status and control capabilities based on the specific characteristics of the vehicle. For example, if the vehicle is a VIP vehicle, the red light at the intersection it is currently passing can be turned green, allowing the vehicle to pass first. Therefore, the main role of vehicle-side applications among the three terminals is the reception and rendering of basic data, and the presentation of cloud-based warning capabilities. The accurate delivery of cloud-based warning capabilities requires an effective and reliable connection between the vehicle-side application and the cloud. If the connection between the two ends is abnormal, the cloud cannot issue roadside warning tasks based on the vehicle's location, nor can it issue cloud-based traffic police tasks.

[0066] The interconnection between the vehicle-side industrial control computer (ICSC) and the cloud mainly involves the CSCCC reporting basic vehicle information such as high-precision positioning and heading information at a fixed frequency, as well as monitoring the status information of multiple nodes in the autonomous driving system. Therefore, if the connection between the CSCCC and the cloud is abnormal, the data monitored by the CSCCC cannot be reported to the cloud, and the vehicle-side safety operator cannot obtain the node status in a timely manner. If the autonomous driving system malfunctions, the driver cannot quickly take over manually. Furthermore, some business scenarios require a matching relationship between the CSCCC and vehicle-side applications. If the connection between the CSCCC and the cloud is abnormal, an effective matching relationship cannot be formed, thus preventing the normal support of multiple business scenarios.

[0067] Based on this, the vehicle-side in this application embodiment needs to detect and obtain the three-terminal connection status of the autonomous vehicle in a timely manner through a connection status detection task.

[0068] It should also be noted that some application scenarios do not completely rely on the fact that all three connection states are in a normal connection state. For example, some business scenarios only require that the connection between the vehicle application and the vehicle industrial control computer is normal. Therefore, those skilled in the art can flexibly adjust how to detect or rely on which connection states to achieve autonomous driving functions according to actual needs, and no specific limitations are made here.

[0069] Step S130: If the connection status detection result is a normal connection status, obtain the corresponding node status detection task.

[0070] When the connectivity of an autonomous vehicle is normal, the corresponding node status detection task can be further obtained. The node status detection task is mainly used to detect the status of various functional nodes of the autonomous vehicle, including software nodes and hardware nodes, so as to ensure the stable operation of the autonomous driving function.

[0071] Step S140: Determine the node state detection result of the autonomous vehicle according to the node state detection task.

[0072] After obtaining the node status detection task, the vehicle can perform a self-check of the node status by executing the node status detection task, thereby obtaining the node status detection result. The node status detection result includes normal nodes and abnormal nodes. For abnormal nodes, it can also include information such as the status description of the abnormal node.

[0073] Step S150: Based on the connection status detection results of the autonomous vehicle and a portion of the node status detection results of the autonomous vehicle, the detection result of each step is output after the detection of each step is completed. Finally, the detection results can be summarized to generate a corresponding self-inspection report for archiving and fault tracing.

[0074] The self-testing method for autonomous vehicles in this application embodiment is based on the special characteristics of autonomous driving scenarios. It efficiently detects the connection status of the vehicle-end application, industrial control computer, and cloud, as well as the status of each node in the autonomous vehicle, saving troubleshooting time and meeting the requirements of autonomous driving scenarios for efficiency, safety, and reliability.

[0075] In one embodiment of this application, obtaining the connection status detection result of the autonomous vehicle according to the connection status detection task includes: detecting the connection status of the autonomous vehicle according to the connection status detection task and obtaining the connection status detection result; and / or, receiving the connection status detection result of the autonomous vehicle fed back from the cloud according to the connection status detection task.

[0076] The acquisition of connectivity status detection results for autonomous vehicles can primarily come from two sources: the vehicle itself and the cloud. The vehicle-side data can be further divided into vehicle-side applications and vehicle-side industrial control units (ICSUs). For the first primary source, the vehicle-side application and the vehicle-side ICS can mutually detect each other's connectivity status, with each end having its own detection timing and log output. Furthermore, the vehicle-side application and the vehicle-side ICS can independently detect their own connectivity status with the cloud using either short or long connections.

[0077] Regarding the second main source, the cloud can determine the normality of the connection status between the vehicle-side end, including the vehicle-side application and the vehicle-side industrial control computer, and the cloud by checking the online status of the vehicle's infotainment system. This is because the vehicle-side application and the vehicle-side industrial control computer have established a binding relationship, which is recognized by the cloud as a group of active devices. At the same time, the cloud can also determine which end has lost connection with the cloud by checking the detailed business processes. For example, if there is no autonomous driving service status monitoring report for a long time, the cloud will consider it as a disconnection from the vehicle-side industrial control computer. If querying the interface to verify that the vehicle is not online, it will be considered as a disconnection from the vehicle-side application.

[0078] Therefore, the three terminals can know their connection status with the other two terminals at any time and manage and maintain it in a certain way.

[0079] In one embodiment of this application, the vehicle-side application and the vehicle-side industrial control computer are matched based on the vehicle-side application identifier and the industrial control computer identifier and stored in the cloud. The connection status detection task includes the vehicle-side application identifier so that the cloud can provide feedback on the connection status of the autonomous vehicle corresponding to the vehicle-side application identifier.

[0080] The vehicle-side application in this embodiment has a vehicle-side application identifier (Serial Number, or SN) that can be used to uniquely identify the vehicle-side application. The vehicle-side industrial control computer also has a unique identification code. After the autonomous vehicle starts or is powered on, the vehicle-side application and the vehicle-side industrial control computer are powered on and connected to the same Wi-Fi network in the vehicle. The vehicle-side industrial control computer is assigned a fixed IP address. The vehicle-side application can actively connect to the IP address of the vehicle-side industrial control computer. The two interconnect and mutually store the vehicle-side application identifier and the unique identification code of the vehicle-side industrial control computer, and report them to the cloud. The cloud identifies the vehicle-side industrial control computer and the vehicle-side application, which hold the same set of vehicle-side application identifiers and unique identification codes, as the same set of active devices, that is, corresponding to the same autonomous vehicle. This completes the matching between the vehicle-side application and the vehicle-side industrial control computer, which facilitates subsequent task distribution by the cloud and business support for various business scenarios.

[0081] Based on this, when the vehicle-side application in this embodiment sends a connection status detection task to the cloud, it can carry its own vehicle-side application identifier. In this way, the cloud can find the unique identification code of the matching vehicle-side industrial control computer based on the vehicle-side application identifier, thereby determining the connection status of the three terminals and feeding it back to the vehicle-side application.

[0082] In one embodiment of this application, after obtaining the connection status detection result of the autonomous vehicle according to the connection status detection task, the method further includes: if the connection status detection result is a normal connection status, transmitting first data between the vehicle-side application and the vehicle-side industrial control computer through the connection between the vehicle-side application and the vehicle-side industrial control computer; if the connection status detection result is a normal connection status, transmitting second data between the vehicle-side application and the cloud through the connection between the vehicle-side application and the cloud; and if the connection status detection result is a normal connection status, transmitting third data between the vehicle-side industrial control computer and the cloud through the connection between the vehicle-side industrial control computer and the cloud.

[0083] If the connection status of the three terminals of the autonomous vehicle is normal, it indicates that normal data transmission can occur between the three terminals. Depending on the functions implemented by each terminal, the specific data transmitted will differ. For example, the connection between the vehicle-side application and the vehicle-side industrial control computer is mainly used to transmit and receive perception and positioning data from the vehicle-side application, as well as support for reverse control of business scenarios, such as triggering and terminating autonomous driving by the vehicle-side application. The interconnection between the vehicle-side application and the cloud mainly involves support for business layer capabilities, such as supporting the cloud to issue V2X warning events like road warnings in real time, supporting demonstration capabilities like issuing traffic police tasks, and supporting the display of traffic light status and the ability to control traffic lights based on the specific characteristics of the vehicle. The interconnection between the vehicle-side industrial control computer and the cloud mainly involves the vehicle-side industrial control computer reporting basic vehicle information such as high-precision positioning information and heading information at a fixed frequency, and also includes status information of multiple nodes monitored for autonomous driving nodes.

[0084] Therefore, the aforementioned "first data", "second data" and "third data" are mainly used to distinguish the data transmitted between the three ends.

[0085] In one embodiment of this application, the step of obtaining the corresponding node status detection task when the connection status detection result is a normal connection status includes: obtaining the corresponding node status detection task when the connection status detection result between the vehicle application and the vehicle industrial control computer is a normal connection status.

[0086] To improve the self-inspection efficiency of the vehicle, the execution of the node status detection task only needs to ensure that the vehicle application and the vehicle industrial control computer are interconnected and the connection status is normal. This can reduce the number of storage nodes in the cloud and save time in obtaining node status detection results.

[0087] Specifically, after the autonomous vehicle starts or powers on, the vehicle-mounted industrial control computer and the vehicle-mounted application start. The prerequisite for interconnection between the vehicle-mounted industrial control computer and the vehicle-mounted application is that they are on the same WiFi network. The vehicle-mounted application first initiates a Socket long connection request to the vehicle-mounted industrial control computer, establishing a connection. The vehicle-mounted application's serial number (SN) is then bound to the unique identifier of the vehicle-mounted industrial control computer. After binding, the vehicle-mounted industrial control computer and the vehicle-mounted application each initiate Socket connection requests to the cloud and upload their binding information. Subsequently, the vehicle-mounted industrial control computer performs a fixed-frequency self-check of the node status, generating data of a preset data type and sending it back to the vehicle-mounted application. In other words, the node status detection results of the vehicle-mounted application originate entirely from the vehicle-mounted industrial control computer. The preset data type here could be, for example, a protocol buffer type, which has high write and parsing efficiency. Of course, those skilled in the art can flexibly use other data types.

[0088] In one embodiment of this application, the step of obtaining the corresponding node state detection task includes: determining the driving state of the autonomous vehicle; when the driving state of the autonomous vehicle is autonomous driving, obtaining a first node state detection task, the first node state detection task including a first node state detection frequency; when the driving state of the autonomous vehicle is non-autonomous driving, obtaining a second node state detection task, the second node state detection task including a second node state detection frequency; wherein, the first node state detection frequency is greater than the second node state detection frequency.

[0089] In real-world scenarios, autonomous vehicles may be in various driving states, such as autonomous driving and non-autonomous driving. The safety and stability requirements of the vehicle may differ in different driving states, and consequently, the requirements for node state detection will also differ.

[0090] Based on this, the vehicle-side in this application embodiment creates different node state detection tasks for different driving states of autonomous vehicles. When obtaining the current node state detection task, it can be combined with the current driving state of the autonomous vehicle to meet the needs of different autonomous driving scenarios. As mentioned above, the driving state of the autonomous vehicle in this application embodiment can be divided into autonomous driving state and non-autonomous driving state. In the autonomous driving state, due to the low degree of human control, once a connection state abnormality occurs, fault handling needs to be carried out in a timely manner. In contrast, in the non-autonomous driving state, the degree of human control is high, and when a node state abnormality occurs, the vehicle's driving can still be manually controlled for a short period of time.

[0091] Therefore, different driving states require different node state detection frequencies for autonomous vehicles. In autonomous driving mode, a higher node state detection frequency (the first node state detection frequency mentioned above) can be set, while in non-autonomous driving mode, a lower node state detection frequency (the second node state detection frequency mentioned above) can be set. For example, a self-check can be performed every 30 seconds in autonomous driving mode and every 60 seconds in non-autonomous driving mode.

[0092] Of course, it should be noted that the driving states classified in the embodiments of this application are not limited to the two mentioned above. Those skilled in the art can flexibly classify more driving states according to actual needs, such as semi-autonomous driving state, etc., which will not be listed here.

[0093] In addition, different autonomous driving states can affect not only the detection frequency of node states, but also the detection frequency of connection states in the aforementioned embodiments. Therefore, different detection frequencies of connection states can be set according to different autonomous driving states.

[0094] In one embodiment of this application, determining the self-test result of the autonomous vehicle based on the connection status detection result and the node status detection result of the autonomous vehicle includes: displaying the connection status detection result on the vehicle according to a first preset display strategy, wherein the first preset display strategy includes connection type and description information of the connection status detection result; and displaying the node status detection result on the vehicle according to a second preset display strategy, wherein the second preset display strategy includes node type, node name and description information of the node status detection result.

[0095] To save troubleshooting time for autonomous vehicles, this application embodiment can also display the connection status detection results and node status detection results of autonomous vehicles on the vehicle side, thereby facilitating timely reminders to relevant personnel to handle abnormal detection results.

[0096] On the one hand, embodiments of this application can adopt a first preset display strategy to display the connection status detection results on the vehicle side. This first preset display strategy can be that when an abnormal connection status is detected, a pop-up reminder of the autonomous vehicle's connection status can be directly displayed on the vehicle side interface. That is, the displayed connection status includes both abnormal and normal connection statuses. For example... Figure 2 As shown, a schematic diagram of the display interface of the connection status detection result of an autonomous vehicle in an embodiment of this application is provided, which specifically presents the connection type and description information of the connection status detection result.

[0097] The reason for displaying the entire connection status of the autonomous vehicle on the vehicle-side interface when abnormal connection states exist is to facilitate troubleshooting by combining it with the connection status of other terminals. For example, if the vehicle-side interface shows an abnormal connection status between the vehicle-side application and the vehicle-side industrial control computer, and the connection status between the vehicle-side industrial control computer and the cloud is also abnormal, but the connection status between the vehicle-side application and the cloud is normal, then the fault is likely located on the vehicle-side industrial control computer. Of course, in addition to the above display method, those skilled in the art can also display only the abnormal connection status according to actual needs.

[0098] On the other hand, embodiments of this application can use a second preset display strategy to display the node status detection results on the vehicle. The second preset display strategy can specifically include node type, node name, and description information of the node status detection results, as shown in Table 1 below, which provides an example of the display content of the node status detection results.

[0099] Table 1

[0100]

[0101]

[0102] As shown in Table 1 above, for the node category of IINIT initialization (self-check) information, there are three specific nodes: IINIT_TIME_SYNC, IINIT_SENSOR_NORMAL, and IINIT_VERSION. When presenting the node status detection results, the detection results of normal nodes and abnormal nodes can be displayed uniformly with fine granularity without distinguishing between them.

[0103] Another strategy is to flexibly adjust the granularity of the presentation based on the node status detection results of each node under each node category. For example, on the one hand, abnormal nodes can be displayed in a fine-grained manner, which makes it easier for relevant personnel to quickly locate the problem nodes and handle the faults. On the other hand, the detection results of a certain normal node category can be displayed in a coarse-grained manner, omitting the detailed information of normal nodes, thereby ensuring the intuitiveness and simplicity of the displayed content.

[0104] The node status detection results shown above are mainly based on the functional classification of the nodes. In this embodiment, nodes can also be classified into hardware nodes and software nodes according to their different attributes. Hardware nodes may include sensor nodes such as GNSS, IMU, LiDAR, cameras, and OBUs, network nodes such as routers, and memory nodes such as CPUs. Software nodes may include / telematics_node (remote information node), / sensor / gnss / drivers_gnss (positioning driver node), and / perception (perception service node), etc. Therefore, this embodiment can create different node status detection tasks for different node types and display the corresponding node status detection results on the vehicle side.

[0105] like Figure 3 As shown, a schematic diagram of the display interface for the node status detection result of a hardware node in an embodiment of this application is provided, such as... Figure 4 As shown, a schematic diagram of the display interface for the node status detection result of a software node in an embodiment of this application is provided.

[0106] The above display strategy enables the presentation of detection results at different levels of fineness for different types of nodes, meeting the detection needs of practical application scenarios. Of course, those skilled in the art can flexibly adjust the specific classification of node types according to actual needs, and no specific limitations are made here.

[0107] In addition, it should be noted that besides pop-up reminders on the vehicle interface, alarms can also be issued and abnormal information can be sent to relevant personnel for troubleshooting so that faults can be handled in a timely manner.

[0108] In one embodiment of this application, the node state detection result includes abnormal nodes. After determining the node state detection result of the autonomous vehicle according to the node state detection task, the method further includes: determining the node type of the abnormal node; determining a corresponding fault handling strategy according to the node type of the abnormal node, so as to process the abnormal node through the fault handling strategy.

[0109] In existing technologies, when an autonomous driving function malfunctions, the most common solution is to restart the vehicle's infotainment system or the vehicle-side application. Alternatively, it may require multiple personnel to find the person who can actually solve the problem, which is relatively time-consuming. Moreover, each restart requires restarting all services, including those that did not malfunction, making this operation very inefficient.

[0110] Based on this, the self-test results obtained by the self-test scheme in this application embodiment can directly locate fine-grained abnormal nodes. The corresponding fault handling strategy can be determined according to the type of abnormal node. Then, the abnormal node can be handled by starting a single abnormal node or by directly contacting the technical or maintenance personnel related to the abnormal node. This can quickly locate and resolve the problem of abnormal node disconnection, and also ensure the effective operation of other service nodes.

[0111] Different types of anomalous nodes reflect different problems, thus often requiring different fault handling strategies. Table 2 below provides a comparison table of anomalous nodes and their corresponding fault handling strategies. While visually presenting the problems of anomalous nodes, Table 2 also provides detailed node anomaly analysis and clear solutions for different anomalous nodes.

[0112] In practical applications, different fault levels can be assigned to different types of nodes. Then, based on these fault levels, the fault handling priorities for different nodes can be determined. Finally, based on these priorities, the fault handling strategies corresponding to each abnormal node can be applied sequentially. The higher the fault level of a node, the greater its impact on the overall implementation of the autonomous driving function, and therefore, the higher its priority for handling.

[0113] Of course, when each abnormal node has an independent function and the corresponding fault handling strategy has little impact on the normal operation of the autonomous driving function when executed simultaneously, fault handling can also be performed on each abnormal node at the same time.

[0114] Table 2

[0115]

[0116]

[0117]

[0118] In one embodiment of this application, after determining the self-test result of the autonomous vehicle based on the connection state detection result and the node state detection result of the autonomous vehicle, the method further includes: storing the self-test result of the autonomous vehicle in the self-test result database of the autonomous vehicle; and recreating the connection state detection task and / or the node state detection task based on the self-test result database.

[0119] The embodiments of this application can uniformly maintain the self-inspection result data of autonomous vehicles over a period of time through the self-inspection result database of autonomous vehicles. On the one hand, this can ensure the traceability of historical self-inspection results, and on the other hand, it can facilitate the optimization of the functions of autonomous vehicles through historical self-inspection result data.

[0120] Based on the historical self-test results stored in the self-test result database, the overall operating status of the autonomous vehicle over a period of time can be statistically analyzed. For example, if the autonomous vehicle's connection status detection results showed anomalies 3 times and node status detection results showed anomalies 4 times in the past 10 days, it indicates a high frequency of anomalies. Therefore, higher connection status detection and node status detection frequencies need to be set subsequently. In other words, the created connection status detection and node status detection tasks can be dynamically adjusted based on statistical analysis results to flexibly adapt the detection tasks to actual detection scenarios. Of course, those skilled in the art can flexibly set the specific adjustments according to actual needs, and no specific limitations are made here.

[0121] In one embodiment of this application, after determining the self-test result of the autonomous vehicle based on the connection status detection result and the node status detection result of the autonomous vehicle, the method further includes: obtaining the self-test result reporting task of the autonomous vehicle; and reporting the self-test result of the autonomous vehicle to the cloud according to the self-test result reporting task.

[0122] After obtaining the self-test results of the autonomous vehicle, this embodiment of the application can also report the self-test results of the autonomous vehicle to the cloud for unified storage and management according to the self-test result reporting task. The self-test result reporting task here may specifically include the vehicle-side application SN or the unique identifier of the vehicle-side industrial control computer, so as to facilitate the cloud to determine which autonomous vehicle's self-test results are from.

[0123] The self-testing method for autonomous vehicles in this application achieves at least the following technical effects:

[0124] 1) Data validity: During the autonomous driving process, the connection status of the vehicle-side application, the vehicle-side industrial control computer, and the cloud is detected to ensure normal connection and thus ensure effective data transmission.

[0125] 2) Data accuracy: After the data is effectively transmitted, the correctness of the data is ensured by detecting the node status, which guarantees the stable and accurate execution of the autonomous driving process.

[0126] 3) Safety: During the autonomous driving process, if any abnormality occurs, the safety operator will be promptly notified to end the autonomous driving mode and switch to manual driving, thus avoiding the occurrence of safety issues.

[0127] 4) Reliability: From large modules to each important node within a module, everything can be clearly presented, enabling precise location of abnormal nodes.

[0128] 5) High efficiency: Problems arising during the autonomous driving process do not require checking logs or lengthy joint debugging processes to identify abnormal nodes; problems can be quickly located and resolved.

[0129] This application embodiment also provides a self-testing device 500 for an autonomous vehicle, the device being applied to the vehicle end, such as... Figure 5 The diagram shows a structural schematic of a self-testing device for an autonomous vehicle according to an embodiment of this application. The device 500 includes: a first acquisition unit 510, a connection state detection unit 520, a second acquisition unit 530, a node state detection unit 540, and a first determination unit 550, wherein:

[0130] The first acquisition unit 510 is used to acquire the connection status detection task of the autonomous vehicle.

[0131] The connection status detection unit 520 is used to obtain the connection status detection results of the autonomous vehicle according to the connection status detection task. The connection status detection results include the connection status detection results between the vehicle application and the vehicle industrial control computer, the connection status detection results between the vehicle application and the cloud, and the connection status detection results between the vehicle industrial control computer and the cloud.

[0132] The second acquisition unit 530 is used to acquire the corresponding node status detection task when the connection status detection result is a normal connection status.

[0133] The node state detection unit 540 is used to determine the node state detection result of the autonomous vehicle according to the node state detection task.

[0134] The first determining unit 550 is used to determine the self-test result of the autonomous vehicle based on the connection status detection result and the node status detection result of the autonomous vehicle.

[0135] In one embodiment of this application, the connection status detection unit 520 is specifically used to: detect the connection status of the autonomous vehicle according to the connection status detection task, and obtain the connection status detection result; and / or, receive the connection status detection result of the autonomous vehicle fed back from the cloud according to the connection status detection task.

[0136] In one embodiment of this application, the apparatus further includes: a first transmission unit, configured to transmit first data between the vehicle-side application and the vehicle-side industrial control computer via a connection between the vehicle-side application and the vehicle-side industrial control computer when the connection status detection result is a normal connection status; a second transmission unit, configured to transmit second data between the vehicle-side application and the cloud via a connection between the vehicle-side application and the cloud when the connection status detection result is a normal connection status; and a third transmission unit, configured to transmit third data between the vehicle-side industrial control computer and the cloud via a connection between the vehicle-side industrial control computer and the cloud when the connection status detection result is a normal connection status.

[0137] In one embodiment of this application, the second acquisition unit 530 is specifically used to: acquire the corresponding node status detection task when the connection status detection result between the vehicle-side application and the vehicle-side industrial control computer is a normal connection status.

[0138] In one embodiment of this application, the second acquisition unit 530: determines the driving state of the autonomous vehicle; when the driving state of the autonomous vehicle is autonomous driving, acquires a first node state detection task, the first node state detection task including a first node state detection frequency; when the driving state of the autonomous vehicle is non-autonomous driving, acquires a second node state detection task, the second node state detection task including a second node state detection frequency; wherein, the first node state detection frequency is greater than the second node state detection frequency.

[0139] In one embodiment of this application, the first determining unit 550 is specifically configured to: display the connection status detection result on the vehicle according to a first preset display strategy, wherein the first preset display strategy includes description information of connection type and connection status detection result; and display the node status detection result on the vehicle according to a second preset display strategy, wherein the second preset display strategy includes description information of node type, node name and node status detection result.

[0140] In one embodiment of this application, the node status detection result includes abnormal nodes, and the device further includes: a second determining unit, configured to determine the node type of the abnormal node; and a third determining unit, configured to determine a corresponding fault handling strategy based on the node type of the abnormal node, so as to process the abnormal node through the fault handling strategy.

[0141] In one embodiment of this application, the device further includes: a third acquisition unit, configured to acquire the self-test result reporting task of the autonomous vehicle; and a reporting unit, configured to report the self-test result of the autonomous vehicle to the cloud according to the self-test result reporting task.

[0142] It is understood that the self-testing device for the aforementioned autonomous vehicle can implement each step of the self-testing method for the autonomous vehicle provided in the foregoing embodiments. The relevant explanations regarding the self-testing method for the autonomous vehicle are applicable to the self-testing device for the autonomous vehicle, and will not be repeated here.

[0143] This application also provides a self-testing system for autonomous vehicles, such as... Figure 6 The diagram shows an architecture diagram of a self-testing system for an autonomous vehicle according to an embodiment of this application. The system includes a vehicle-side terminal and a cloud-side terminal. The vehicle-side terminal includes a vehicle-side application and a vehicle-side industrial control computer / domain controller. The vehicle-side terminal is used to execute any of the methods described above.

[0144] The self-testing system of the autonomous vehicle through the embodiments of this application can know the connection status between any end of the system and the other two ends at any time and manage and maintain it in a certain way, thus meeting the requirements of autonomous driving scenarios for efficiency, safety and reliability.

[0145] Figure 7 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Please refer to it. Figure 7 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk drive. Of course, the electronic device may also include other hardware required for other business operations.

[0146] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 7 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0147] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0148] The processor reads the corresponding computer program from non-volatile memory into main memory and then runs it, forming the self-testing device of the autonomous vehicle at the logical level. The processor executes the program stored in memory and specifically performs the following operations:

[0149] Task: Obtain connectivity status detection for autonomous vehicles;

[0150] Based on the connection status detection task, the connection status detection results of the autonomous vehicle are obtained. The connection status detection results include the connection status detection results between the vehicle application and the vehicle industrial control computer, the connection status detection results between the vehicle application and the cloud, and the connection status detection results between the vehicle industrial control computer and the cloud.

[0151] If the connection status detection result is a normal connection status, obtain the corresponding node status detection task;

[0152] Based on the node state detection task, determine the node state detection result of the autonomous vehicle;

[0153] The self-test result of the autonomous vehicle is determined based on the connection status detection result and the node status detection result of the autonomous vehicle.

[0154] The above is as stated in this application. Figure 1The method executed by the self-testing device of the autonomous vehicle disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0155] The electronic device can also perform Figure 1 The method for executing the self-test device of an autonomous vehicle, and the implementation of the self-test device of an autonomous vehicle in... Figure 1 The functions of the embodiments shown are not described again in this application.

[0156] This application also proposes a computer-readable storage medium that stores one or more programs, the programs including instructions that, when executed by an electronic device including multiple applications, enable the electronic device to perform... Figure 1 The self-testing device of the autonomous vehicle in the illustrated embodiment executes a method, specifically used to perform:

[0157] Task: Obtain connectivity status detection for autonomous vehicles;

[0158] Based on the connection status detection task, the connection status detection results of the autonomous vehicle are obtained. The connection status detection results include the connection status detection results between the vehicle application and the vehicle industrial control computer, the connection status detection results between the vehicle application and the cloud, and the connection status detection results between the vehicle industrial control computer and the cloud.

[0159] If the connection status detection result is a normal connection status, obtain the corresponding node status detection task;

[0160] Based on the node state detection task, determine the node state detection result of the autonomous vehicle;

[0161] The self-test result of the autonomous vehicle is determined based on the connection status detection result and the node status detection result of the autonomous vehicle.

[0162] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0163] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0164] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0165] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0166] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0167] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0168] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0169] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0170] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0171] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A self-testing method for an autonomous vehicle, wherein, The method is executed by the vehicle end, and the method includes: Task: Obtain connectivity status detection for autonomous vehicles; Based on the connection status detection task, the connection status detection results of the autonomous vehicle are obtained. The connection status detection results include the connection status detection results between the vehicle application and the vehicle industrial control computer, the connection status detection results between the vehicle application and the cloud, and the connection status detection results between the vehicle industrial control computer and the cloud. If the connection status detection result is a normal connection status, obtain the corresponding node status detection task; Based on the node state detection task, determine the node state detection result of the autonomous vehicle; The self-test result of the autonomous vehicle is determined based on the connection status detection result and the node status detection result of the autonomous vehicle.

2. The method as described in claim 1, wherein, The step of obtaining the connection state detection result of the autonomous vehicle according to the connection state detection task includes: Based on the connectivity status detection task, the connectivity status of the autonomous vehicle is detected, and the connectivity status detection result is obtained; or... According to the connection status detection task, the connection status detection results of the autonomous vehicle are received from the cloud.

3. The method as described in claim 1, wherein, After obtaining the connectivity state detection results of the autonomous vehicle according to the connectivity state detection task, the method further includes: If the connection status detection result is a normal connection status, the first data is transmitted between the vehicle application and the vehicle industrial control computer through the connection between the vehicle application and the vehicle industrial control computer. If the connection status detection result is a normal connection status, the second data is transmitted between the vehicle application and the cloud through the connection between the vehicle application and the cloud. If the connection status detection result is a normal connection status, third data is transmitted between the vehicle-mounted industrial control computer and the cloud through the connection between the vehicle-mounted industrial control computer and the cloud.

4. The method as described in claim 1, wherein, When the connection status detection result is a normal connection status, the corresponding node status detection task includes: If the connection status detection result between the vehicle-side application and the vehicle-side industrial control computer is normal, the corresponding node status detection task is obtained.

5. The method as described in claim 1, wherein, The task of obtaining the corresponding node status detection includes: Determine the driving status of the autonomous vehicle; When the autonomous vehicle is in autonomous driving mode, a first node state detection task is obtained, and the first node state detection task includes a first node state detection frequency. When the driving state of the autonomous vehicle is in a non-autonomous driving state, a second node state detection task is obtained, and the second node state detection task includes a second node state detection frequency. The first node status detection frequency is greater than the second node status detection frequency.

6. The method of claim 1, wherein, The step of determining the self-test result of the autonomous vehicle based on the connection status detection result and the node status detection result of the autonomous vehicle includes: According to the first preset display strategy, the connection status detection result is displayed on the vehicle end. The first preset display strategy includes description information of connection type and connection status detection result. According to the second preset display strategy, the node status detection results are displayed on the vehicle end. The second preset display strategy includes node type, node name, and descriptive information of the node status detection results.

7. The method of claim 1, wherein, The node state detection results include abnormal nodes. After determining the node state detection results of the autonomous vehicle based on the node state detection task, the method further includes: Determine the node type of the abnormal node; Based on the node type of the abnormal node, a corresponding fault handling strategy is determined, and the abnormal node is handled through the fault handling strategy.

8. The method of claim 1, wherein, After determining the self-test result of the autonomous vehicle based on the connection status detection result and the node status detection result of the autonomous vehicle, the method further includes: The task is to obtain and report the self-inspection results of the autonomous vehicle. Based on the self-inspection results, the task is to report the self-inspection results of the autonomous vehicle to the cloud.

9. A self-testing device for an autonomous vehicle, wherein, The device is applied to a vehicle end, and the device includes: The first acquisition unit is used to acquire the connection status detection task of the autonomous vehicle. A connection status detection unit is used to obtain the connection status detection results of the autonomous vehicle according to the connection status detection task. The connection status detection results include the connection status detection results between the vehicle application and the vehicle industrial control computer, the connection status detection results between the vehicle application and the cloud, and the connection status detection results between the vehicle industrial control computer and the cloud. The second acquisition unit is used to acquire the corresponding node status detection task when the connection status detection result is a normal connection status. A node state detection unit is used to determine the node state detection result of the autonomous vehicle based on the node state detection task. The first determining unit is used to determine the self-test result of the autonomous vehicle based on the connection status detection result and the node status detection result of the autonomous vehicle.

10. A self-testing system for an autonomous vehicle, wherein, The system includes a vehicle terminal and a cloud terminal. The vehicle terminal includes a vehicle terminal application and a vehicle terminal industrial control computer. The vehicle terminal is used to execute any of the methods described in claims 1 to 8.

11. An electronic device, comprising: processor; as well as A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the method of any one of claims 1 to 8.

12. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of applications, cause the electronic device to perform the method of any one of claims 1 to 8.

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