Control method and system of full-unmanned automatic driving vehicle

Abstract

The embodiment of the invention discloses a control method and system for a full unmanned automatic driving vehicle. The method comprises the steps that tasks are distributed to at least one unmanned automatic driving vehicle according to preset scheduling time; acquiring vehicle information of a target vehicle in the preset scheduling time; determining abnormal information of the target vehicle according to the vehicle information; performing early warning on an abnormal condition corresponding to the abnormal information; and issuing a remote control instruction to the target vehicle according to the abnormal situation, so as to execute a corresponding operation based on the remote control instruction when the target vehicle cannot take over and remove the abnormity through a standby system. Therefore, by monitoring the performance of the shift arrangement time of the unmanned automatic driving vehicle, the abnormal problem of the vehicle can be found and solved in time, and the safety and reliability of the unmanned automatic driving vehicle are improved.

Description

Technical Field

[0001] The present invention relates to the field of autonomous driving technology, and in particular to a control method and system for a fully unmanned autonomous driving vehicle. Background Art

[0002] With the development of autonomous driving technology, unmanned operation has become an important trend in the industry.

[0003] However, since the current research on vehicle operation and dispatch is mainly based on conventional vehicles with human drivers and autonomous vehicles with safety officers, there is still a lot of room for research on the large-scale operation of autonomous vehicles in a fully unmanned situation. For example, before realizing unmanned operation, unmanned road testing is required to comprehensively monitor the status of the vehicle, so as to promptly detect and handle abnormal situations and ensure the safety and reliability of unmanned autonomous vehicles. Summary of the invention

[0004] In view of this, an object of an embodiment of the present invention is to provide a control method and system for a fully unmanned autonomous driving vehicle to improve the safety and reliability of the unmanned autonomous driving vehicle.

[0005] In a first aspect, an embodiment of the present invention is to provide a control method for a fully unmanned autonomous driving vehicle, the method comprising:

[0006] Dispatching tasks to at least one unmanned autonomous driving vehicle according to a preset schedule, wherein the tasks include road test tasks and order tasks;

[0007] Obtaining vehicle information of the target vehicle within the preset scheduling time;

[0008] Determining abnormal information of the target vehicle according to the vehicle information, wherein the abnormal information includes an abnormal situation;

[0009] Providing early warning of the abnormal situation;

[0010] A remote control instruction is issued to the target vehicle according to the abnormal situation, so that a corresponding operation is performed based on the remote control instruction when the target vehicle cannot resolve the abnormality through the backup system taking over.

[0011] Furthermore, the method further comprises:

[0012] The task details of the target vehicle are displayed on the display interface, and the task details include task attributes and status information. The task attributes include one or more of the task name, task execution time, task starting point and task end point. The status information is used to characterize the task execution status of the vehicle, and the task execution status includes receiving orders, picking up the vehicle, waiting, sending the vehicle off, completed and / or idle.

[0013] Further, a cancellation control is also displayed on the display interface, and the method further includes:

[0014] In response to the cancellation control being triggered, controlling the target vehicle to go offline or stop.

[0015] Further, the method further includes:

[0016] In response to the target vehicle successfully receiving an order, controlling the target vehicle to start performing a task.

[0017] Further, the method further includes:

[0018] In response to the preset scheduling time ending and the target vehicle having unfinished tasks, controlling the target vehicle to go offline or stop after all tasks are completed.

[0019] Further, the abnormal information further includes an abnormal level, and warning the abnormal situation includes:

[0020] Warning the abnormal situation based on the abnormal level of the abnormal situation.

[0021] Further, warning the abnormal situation based on the abnormal level of the abnormal situation includes:

[0022] Respectively displaying on the display interface each of the abnormal situations whose abnormal level reaches a preset level;

[0023] Foldingly displaying on the display interface each of the abnormal situations whose abnormal level does not reach the preset level.

[0024] Further, the method further includes:

[0025] In response to the abnormal situation disappearing, ending the warning.

[0026] Further, the vehicle information includes the fault detection result of the intelligent driving system, and determining the abnormal information of the target vehicle according to the vehicle information includes:

[0027] In response to the fault detection result indicating a system abnormality, determining that the abnormal situation of the target vehicle is a system abnormality, and the system abnormality is used to represent a hardware abnormality or a software abnormality.

[0028] Further, the vehicle information includes operation and maintenance information, and determining the abnormal information of the target vehicle according to the vehicle information includes:

[0029] Judging the operation and maintenance status of the target vehicle according to the operation and maintenance information, and the operation and maintenance status is a communication connection status or a sustainable state;

[0030] In response to an abnormality in the operation and maintenance status, it is determined that the abnormal situation of the target vehicle is an operation and maintenance abnormality, and the abnormality in the operation and maintenance status is used to indicate that the vehicle is out of contact or the vehicle cannot continue to travel.

[0031] Further, the vehicle information includes driving information, and determining the abnormal information of the target vehicle according to the vehicle information includes:

[0032] Determining the driving state of the target vehicle according to the driving information;

[0033] In response to the abnormality in the driving state, it is determined that the abnormal situation of the target vehicle is a driving abnormality, and the abnormality in the driving state is used to indicate that the vehicle is stopped or blocked.

[0034] Further, the vehicle information includes task information, and determining the abnormal information of the target vehicle according to the vehicle information includes:

[0035] Determine the mission status of the target vehicle according to the mission information;

[0036] In response to the abnormality of the task status, the abnormal situation of the target vehicle is determined to be a task abnormality, and the abnormality of the task status is used to indicate that the vehicle task execution is wrong.

[0037] In a second aspect, an embodiment of the present invention is intended to provide a control system for a fully unmanned autonomous driving vehicle, the system comprising:

[0038] A task module, used to dispatch tasks to at least one unmanned autonomous driving vehicle according to a preset scheduling time, wherein the tasks include road test tasks and order tasks;

[0039] A monitoring module, used to obtain vehicle information of a target vehicle within the preset scheduling time;

[0040] An alarm module, used to determine abnormal information of the target vehicle according to the vehicle information, wherein the abnormal information includes an abnormal situation; and to issue an early warning for the abnormal situation;

[0041] The remote control module is used to send a remote control instruction to the target vehicle according to the abnormal situation, so as to perform a corresponding operation based on the remote control instruction when the vehicle cannot resolve the abnormality through the backup system.

[0042] In a third aspect, an embodiment of the present invention is to provide an electronic device, comprising a memory and a processor, wherein the memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement any of the methods described above.

[0043] In a fourth aspect, an embodiment of the present invention aims to provide a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method steps as described in any one of the above items are implemented.

[0044] The technical solution of the embodiment of the present invention dispatches tasks to at least one unmanned autonomous driving vehicle according to a preset shift schedule, obtains the vehicle information of the target vehicle within the preset shift schedule, determines the abnormal information of the target vehicle according to the vehicle information, and issues an early warning for the abnormal situation corresponding to the abnormal information; sends a remote control instruction to the target vehicle according to the abnormal situation, so that the target vehicle can perform corresponding operations based on the remote control instruction when the target vehicle cannot resolve the abnormality through the backup system. Therefore, by monitoring the performance of the unmanned autonomous driving vehicle within the shift schedule, it is possible to timely discover and resolve vehicle abnormalities, thereby improving the safety and reliability of the unmanned autonomous driving vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The above and other objects, features and advantages of the present invention will become more apparent through the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:

[0046] Figure 1 is an architecture diagram of a fully unmanned autonomous driving vehicle task management system according to an embodiment of the present invention;

[0047] Figure 2 is a flow chart of a control method for a fully unmanned automatic driving vehicle in an embodiment of the present invention;

[0048] Figure 3 is a schematic diagram of a display interface of an embodiment of the present invention;

[0049] Figure 4 is another schematic diagram of a display interface according to an embodiment of the present invention;

[0050] Figure 5 is a warning strategy table for abnormal situations in an embodiment of the present invention;

[0051] Figure 6 is a schematic diagram of a control system of a fully unmanned autonomous driving vehicle according to an embodiment of the present invention;

[0052] Figure 7 is a schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The present application will be described based on embodiments, but the present application is not limited to these embodiments. In the following detailed description of the present application, some specific details are described in detail. Those skilled in the art can fully understand the present application without the description of these details. In order to avoid obscuring the essence of the present application, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0054] In addition, those of ordinary skill in the art should understand that the accompanying drawings provided herein are for illustrative purposes only and are not necessarily drawn to scale.

[0055] Unless the context clearly requires otherwise, the words such as "including" and "comprising" in the entire application document shall be construed in an inclusive sense rather than an exclusive or exhaustive sense; that is, it is the meaning of "including but not limited to".

[0056] In the description of the present application, it should be understood that the terms "first", "second", etc. are only used for descriptive purposes and cannot be construed as indicating or implying relative importance. In addition, in the description of the present application, unless otherwise specified, the meaning of "a plurality of" is two or more.

[0057] For the solutions described in this specification and the embodiments, if they involve personal information processing, they will all be processed on the premise of having a legal basis (such as obtaining the consent of the personal information subject, or being necessary for performing a contract, etc.), and will only be processed within the specified or agreed scope. If the user refuses to process personal information other than the necessary information required for the basic functions, it will not affect the user's use of the basic functions.

[0058] Monitoring the abnormal state of unmanned autonomous vehicles is of great significance for improving the safety and reliability of unmanned autonomous vehicles. However, the existing monitoring systems for autonomous vehicles mainly obtain the state data of the vehicle through various sensors and data acquisition devices for analysis to determine whether the vehicle is abnormal. At the same time, the existing monitoring systems only support displaying the location information and service status of the vehicle. When an abnormal situation occurs in the vehicle, it can only be synchronized and processed offline and does not have the cloud processing ability. In view of this, the embodiments of the present invention aim to provide a control method for fully unmanned autonomous vehicles to achieve cloud management and control of fully unmanned autonomous vehicles and improve the safety and reliability of unmanned autonomous driving.

[0059] Figure 1 is the architecture diagram of the task management system for fully unmanned autonomous vehicles according to the embodiments of the present invention. As Figure 1As shown in the figure, the driverless autonomous vehicle management system in this embodiment includes at least one vehicle C and a server S. Among them, each vehicle C is communicatively connected to the server S, and each vehicle C is a fully driverless autonomous vehicle. The server S can be a single server or a server cluster in the platform cloud. In this embodiment, the server S manages tasks for each vehicle C, including dispatching tasks, controlling the vehicle C to start processing tasks, and performing anomaly handling in a timely manner when an anomaly occurs in the vehicle, etc.; the vehicle C, as the executor of the driving activity, receives the tasks dispatched by the server S, processes the received tasks, and transmits the order execution status in real time, such as the transmission of statuses such as completion and anomaly, and conveys them to remote personnel for cloud processing. Thus, through the interaction and cooperation between the server and each driverless autonomous vehicle, the processing efficiency of driving tasks can be improved.

[0060] Meanwhile, to improve the safety and reliability during the use of driverless autonomous vehicles, in this embodiment, the control method of fully driverless autonomous vehicles is executed in the cloud to monitor and manage the use process of driverless autonomous vehicles, so as to discover and handle anomalies in a timely manner, avoid unnecessary dangers, and thus improve the safety and reliability of driverless autonomous vehicles.

[0061] Figure 2 is a flowchart of the control method of fully driverless autonomous vehicles in an embodiment of the present invention. As Figure 2 shown, the control method of fully driverless autonomous vehicles in this embodiment includes the following steps.

[0062] In step S100, tasks are dispatched to at least one driverless autonomous vehicle according to a preset scheduling time.

[0063] In this embodiment, when the cloud initiates tasks to each driverless autonomous vehicle, tasks are dispatched to at least one driverless autonomous vehicle according to a preset scheduling time. Optionally, the preset scheduling time in this embodiment is the working time of the driverless autonomous vehicle. For example, 8:00 - 18:00. Since the driverless autonomous vehicle has a general autonomous driving function, within the working time of the driverless autonomous vehicle (i.e., the preset scheduling time), it is defaulted that the driverless autonomous vehicle is in a ready-to-receive-order state at any time. Once it receives the task start instruction from the cloud (i.e., the server), it can execute the corresponding task according to the task requirements.

[0064] Before an autonomous driverless vehicle enters the operation stage, road test tasks need to be carried out to optimize its performance based on the performance of the autonomous driverless vehicle in the road test tasks, so that the vehicle can have strong driving task processing capabilities after being put into operation, ensuring the safety and reliability of driving. Moreover, after the autonomous driverless vehicle completes the road test tasks, the autonomous driverless vehicle that passes the road test will be put into operation to execute corresponding order tasks (including travel, transportation, etc.). Among them, the road test task usually refers to the task of driving and detecting an autonomous driverless vehicle on the road, which is used to test the driving performance of the autonomous driverless vehicle. The order task is the driving task executed after the autonomous driverless vehicle is put into operation. Therefore, the tasks in this embodiment are described by taking the road test task and the order task as examples. However, it should be understood that the tasks in this embodiment are only examples, and the types of tasks can also include other driving tasks executed by autonomous driverless vehicles.

[0065] Optionally, to facilitate the cloud to understand the task processing situation of each autonomous driverless vehicle and coordinate the task allocation of each autonomous driverless vehicle in the platform, the task details of each autonomous driverless vehicle will be displayed on the display interface of the cloud in this embodiment. Among them, the task details include task attributes and status information. The task attributes include one or more of the task name, task execution time, task start point, and task end point. The status information is used to characterize the task execution status of the vehicle, and the task execution status includes listening for orders, picking up passengers, waiting, dropping off passengers, completing, and / or being idle, etc. It should be noted that the road test tasks executed by the autonomous driverless vehicle in this embodiment can be used to simulate actual order tasks and have the same processing process and task execution status as the order tasks.

[0066] Furthermore, the cloud in this embodiment can manage multiple autonomous driverless vehicles simultaneously, and there are multiple levels of pages on the display interface. For example, multiple autonomous driverless vehicles are displayed on the first-level page, and different autonomous driverless vehicles are distinguished by vehicle identifiers. The vehicle identifier can be a digital code in a preset format or other forms of characters (such as letters, special symbols, etc.) or a combination of characters. After the cloud personnel select one of the autonomous driverless vehicles as the target vehicle, the display page jumps from the first-level page to the second-level page, and the relevant information corresponding to the target vehicle is displayed on the second-level page. Below, the display interface of the target vehicle's relevant information is used as an example for description.

[0067] Figure 3 It is a schematic diagram of the display interface of the embodiment of the present invention. As Figure 3As shown in the figure, on the display interface P in this embodiment, a control panel P1, a vehicle card P2, and a map P3 are displayed. The control panel P1 part includes multiple function options. Selecting one of the function options can enter the corresponding function interface, and different function options correspond to different display contents. When the function option is "Business Monitoring", the content shown in the figure will be displayed. In the vehicle card P2 part, vehicle details and task details are displayed. Among them, the vehicle details include vehicle basic information, and the vehicle basic information includes one or more of relevant information such as vehicle appearance pictures, vehicle identifiers, vehicle driving types, and system versions. The task details include task attributes and status information. The task attributes include one or more of task names, task execution times, task start points, and task end points. The status information is used to characterize the task execution status of the vehicle, and the task execution status includes listening for orders, picking up passengers, waiting, dropping off passengers, completing, and canceling. Specifically, when key operations such as parking or sudden acceleration / deceleration occur to the target vehicle, a pop-up display can also be made on the display interface. In the map P3 part, with the road network as the background, position identifiers of the task start point and task end point of the target vehicle, the route from the task start point to the task end point, and the real-time position of the vehicle are displayed on the road network.

[0068] Optionally, as Figure 3 shown, a task information control 31 is also displayed on the display interface in this embodiment. When a person in the cloud selects the task information control 31, the display interface is controlled to display the task details. Further, a task mode control 32 is also displayed on the display interface in this embodiment, and the words "Automatic Order Assignment" are displayed on the task mode control 32. When the task mode control 32 is selected and triggered, the cloud system automatically assigns an order to the target vehicle, and the target vehicle automatically listens for the order and spontaneously starts task processing when the task arrives. The corresponding control method also includes that the cloud responds to the successful listening for the order by the target vehicle and controls the target vehicle to start executing the task.

[0069] Optionally, as Figure 3 shown, a cancellation control 33 is also displayed on the display interface in this embodiment. The cancellation control 33 is displayed in the vehicle card part and is used to end the execution process of the current task, perform a parking operation, or control the target vehicle to go offline. The corresponding control method for the driverless autonomous vehicle also includes that the cloud responds to the triggering of the cancellation control and controls the target vehicle to go offline or park. Another option is that in this embodiment, after the cancellation control 33 is triggered, a cancellation prompt can be first popped up to confirm the operation corresponding to the cancellation control. For example, the cloud can display a prompt message "Are you sure to cancel the task? The cancelled task does not support restarting" after detecting that the cancellation control 33 is triggered, so that the person in the cloud can view the prompt message and confirm the operation of ending the task, which can avoid the impact of no operation on task processing. Further, in this embodiment, after controlling the target vehicle to go offline, the relevant information of the target vehicle will also stop being displayed on the display interface.

[0070] It should be noted that the available state of the cancellation control 33 in this embodiment is related to the corresponding task. At the same time, to facilitate the distinction of the tasks performed by the driverless vehicle, a test control 34 will be displayed on the display interface in this embodiment. When the test control 34 is selected, it indicates that the task performed by the target vehicle is a road test task. When the task performed by the target vehicle is a road test task, the cancellation control 33 in this embodiment is in an available state throughout the scheduled time, so that the cloud personnel can intervene and repair the target vehicle at any time to ensure the safe and reliable operation of the target vehicle. When the test control 34 is not selected, it indicates that the task performed by the target vehicle is an order task or other non-road test tasks. At this time, the cancellation control 33 is only in an available state during the time interval after the task is completed and before the next task arrives, so as to avoid the influence of cloud control on the current order processing process.

[0071] Optionally, as Figure 3 shown, a prompt control 35 is also displayed on the display interface in this embodiment. Usually, the target vehicle will complete the corresponding task to be processed within the scheduled time. At this time, the scheduled time is displayed in a countdown manner through the prompt control 35, for example, it shows "There are still 3 minutes until the scheduled time". However, in some special cases, for example, when the scheduled time arrives but the task of the target vehicle has not been completed, if the first requirement is to complete the task, at this time, the target vehicle needs to continue to execute the task until all tasks to be processed are completed after the scheduled time ends. For example, when the task currently performed by the target vehicle is the last task, a text similar to "The current task has exceeded the scheduled time" will be displayed on the prompt control 35; or, when there are still tasks to be processed after the task currently performed by the target vehicle is completed, a text similar to "The current task has exceeded the scheduled time, start the next task after completion" will be displayed on the prompt control 35. Correspondingly, the control method in this embodiment further includes that the cloud controls the target vehicle to go offline or stop after all tasks are completed in response to the end of the preset scheduled time and the existence of unfinished tasks of the target vehicle. And, by displaying words such as "The current task has exceeded the scheduled time" through the prompt control to prompt the cloud personnel, this is beneficial to accelerating the task processing process.

[0072] Optionally, a health status control 36 is also displayed on the display interface in this embodiment. When the health status control 36 is selected, the vehicle part will display the fault detection result uploaded by the target vehicle end. At the same time, when the fault detection result indicates that the target vehicle has a software anomaly, the number of anomalies is displayed on the health status words in the vehicle card part, and the number of anomalies can be displayed in a prominent color or symbol style, etc., so that the cloud personnel can understand the fault detection result detected by the target vehicle based on its own driving system.

[0073] Optionally, a map operation control 37 is also displayed on the display interface of this embodiment. The map operation control 37 includes option controls such as zoom in, zoom out, positioning, and closing. Thus, the personnel in the cloud can zoom in, zoom out, position, or close the map display by selecting different option controls in the map operation control 37.

[0074] It should be understood that the presentation modes of the display interface in this embodiment include but are not limited to the above display content, display position, display effect, and combinations.

[0075] Furthermore, in order to understand the usage performance of the target vehicle and improve the safety and reliability of the unmanned autonomous vehicle, the target vehicle within the scheduled shift time will be monitored and abnormal analyzed in this embodiment.

[0076] In step S200, vehicle information of the target vehicle within the preset scheduled shift time is obtained.

[0077] In this embodiment, an unmanned autonomous vehicle is taken as an example of the target vehicle for illustration. Multiple unmanned autonomous vehicles under the same cloud platform can adopt the same control method as the target vehicle.

[0078] Meanwhile, in order to timely discover and understand the usage situation of the vehicle and facilitate the precise control of the vehicle, the vehicle information in this embodiment includes the basic information of the vehicle and other characteristic information reflecting the driving health state of the vehicle. The basic information includes attribute information such as the vehicle identification and vehicle driving type of the vehicle. Thus, in this embodiment, different vehicles can be judged and distinguished through the basic information of the vehicle, which is beneficial to the precise control of different vehicles and the target vehicle, and the health state and task execution situation of the vehicle can be analyzed through the characteristic information of the vehicle, which is convenient for timely discovering and understanding vehicle anomalies and abnormal task executions, and then solving the anomalies in time when they occur, improving the safety and reliability of the unmanned autonomous vehicle.

[0079] Optionally, in this embodiment, one or more of the fault detection results, operation and maintenance information, driving information, and task information generated during the scheduled shift time of the target vehicle are used as the characteristic information reflecting the driving health state of the vehicle. Correspondingly, in addition to the basic information of the vehicle, the vehicle information also includes one or more of the fault detection results, operation and maintenance information, driving information, and task information of the intelligent driving system. And the more types of information included in the vehicle information, the more comprehensive the vehicle information obtained by the cloud, the higher the coverage of the abnormal analysis of the target vehicle, which is more beneficial to the performance optimization of the unmanned autonomous vehicle and the improvement of the safety and reliability of the autonomous driving performance.

[0080] Furthermore, for the convenience of understanding, different types of vehicle information will be described in this embodiment.

[0081] In this embodiment, the driverless autonomous vehicle has two systems, including an intelligent driving system (also known as a safety driving system, abbreviated as ss) and a backup system (also known as a fallback system). Under normal circumstances, the intelligent driving system, as the first main system, controls the operation of the driverless autonomous vehicle, and can detect vehicle faults, determine the fault detection results, and reflect whether the vehicle is abnormal through the fault detection results. The backup system, as the second system in the driverless autonomous vehicle, can also take over the vehicle to ensure the smooth operation and stop of the vehicle. Based on this, in this embodiment, when the driverless autonomous vehicle is abnormal, the backup system is preferentially used to intervene in time to take over the driverless autonomous vehicle and control the vehicle driving operation, such as controlling the vehicle to stop driving or driving to the target parking point to wait for rescue, so as to avoid dangerous behaviors caused by the vehicle continuing to drive on the road, improve the safety of the driverless autonomous vehicle, and at the same time help improve the efficiency of exception handling. However, when the backup system cannot take over smoothly or the abnormality still exists after taking over, it is still necessary to initiate remote assistance (RA) to the cloud to ensure that the vehicle abnormality can be detected and processed in time.

[0082] Based on this, optionally, the objects of fault detection in this embodiment include the vehicle hardware part (such as vehicle hardware such as sensor hardware and tires) and the software part (such as perception nodes, planning nodes, etc.). The abnormal situations corresponding to the fault detection results are hardware abnormalities and / or software abnormalities, and the covered range of abnormal situations can include the situation where the backup system takeover fails or the abnormality still exists after the backup system takes over, or can also represent all abnormalities that occur in the intelligent driving system, including the abnormalities of the intelligent driving system, the situation where the backup system takeover fails or the abnormality still exists after the backup system takes over.

[0083] Furthermore, after detecting a system abnormality, the target vehicle in this embodiment will display the abnormality on the target vehicle (such as turning on the node red light, etc.), and at the same time send the fault detection result to the cloud through the target vehicle, and inform the cloud whether the target vehicle is abnormal through the fault detection result. After receiving the fault detection result sent by the target vehicle, the cloud directly determines the abnormal situation that occurs to the target vehicle based on the fault detection result.

[0084] Optionally, in this embodiment, the operation and maintenance information is used to characterize the operation and maintenance information related to the vehicle driving process, including vehicle heartbeat, vehicle fuel and power information, etc. Among them, the vehicle heartbeat can reflect the communication connection status between the driverless vehicle and the cloud, and the vehicle fuel and power information can reflect the endurance ability of the driverless vehicle. At the same time, the driving information is used to characterize the relevant parameters during the vehicle driving process, including driving position, driving time, etc. The task information is used to characterize the task information related to the vehicle task processing. The task information includes task attributes and status information, etc. The task attributes include one or more of the task name, task execution time, task start point, and task end point. The status information is used to characterize the task execution status of the vehicle. The task execution status includes listening for orders, picking up passengers, waiting, dropping off passengers, completing, and / or being idle.

[0085] In step S300, the abnormal information of the target vehicle is determined according to the vehicle information. Among them, the abnormal information includes abnormal situations.

[0086] In this embodiment, after receiving the vehicle information sent by the target vehicle, the cloud determines the abnormal information of the target vehicle according to the vehicle information. Specifically, since the vehicle information can reflect the usage of the vehicle from multiple dimensions, the abnormal information determined based on the vehicle information will include various abnormal situations, and different abnormal situations can have corresponding determination methods.

[0087] Optionally, in this embodiment, when determining the abnormal information of the target vehicle according to the fault detection result of the intelligent driving system in the vehicle information, since the abnormality corresponding to the fault detection result can be directly determined by the vehicle end of the target vehicle, the cloud determines that the abnormal situation of the target vehicle is a system abnormality in response to the system abnormality indicated by the fault detection result. Among them, the system abnormality is used to characterize hardware abnormality or software abnormality. Thus, in this embodiment, without analyzing the vehicle information, the system abnormality existing in the target vehicle can be directly determined according to the fault detection result uploaded by the target vehicle, which is beneficial to improving the overall abnormal monitoring efficiency.

[0088] Optionally, in this embodiment, when determining the abnormal information of the target vehicle according to the operation and maintenance information in the vehicle information, the cloud first judges the operation and maintenance status of the target vehicle according to the operation and maintenance information, and then determines whether the target vehicle has an operation and maintenance abnormality based on the operation and maintenance status. Further, the operation and maintenance status is the communication connection status, the sustainable state, or other states affecting vehicle operation and maintenance. At the same time, the cloud determines that the abnormal situation of the target vehicle is an operation and maintenance abnormality in response to the abnormal operation and maintenance status (such as the target vehicle being out of contact, the target vehicle running out of fuel and power and being unable to continue driving, etc.).

[0089] Specifically, taking the operation and maintenance status as the communication connection status and / or the sustainable cruising status as an example, the communication connection status in this embodiment is determined by the aforementioned vehicle heartbeat, and the sustainable cruising status is determined by the aforementioned fuel and electricity information. The target vehicle will send the vehicle heartbeat and / or fuel and electricity information to the cloud regularly or periodically. When the cloud does not detect the vehicle heartbeat after a predetermined time or the corresponding duration of a predetermined period (for example, 5 s), and / or the fuel quantity value corresponding to the received fuel and electricity information is lower than the predetermined fuel quantity threshold (such as, 25 L, or 1 / 3 of the fuel tank capacity) or the power is lower than the power threshold (such as, 20% of the full charge power), it indicates that the target vehicle is out of contact (that is, the communication connection status is abnormal) and / or the target vehicle has insufficient sustainable cruising ability (that is, the sustainable cruising status is abnormal). At this time, it is determined that the communication connection status and / or the sustainable cruising status of the target vehicle is abnormal.

[0090] Optionally, in this embodiment, when determining the abnormal information of the target vehicle according to the driving information in the vehicle information, the cloud first determines the driving status of the target vehicle according to the driving information, and then determines whether the target vehicle has a driving abnormality based on the driving status. Further, the driving status in this embodiment is used to characterize that the vehicle driving pauses or is blocked, and the reasons for the pause or blockage include road construction, traffic police command, etc. The cloud determines whether the target vehicle driving has a pause or blockage by the position change of the target vehicle within a predetermined time length or the path planning frequency. When the target vehicle stays at the same position or within a certain area within the predetermined time length, or the target vehicle performs multiple path planning (that is, performs a rerouting operation) at the same position, it indicates that the target vehicle driving has a pause or blockage, that is, the driving status of the target vehicle is abnormal. At this time, in response to the abnormal driving status, the cloud determines that the abnormal situation of the target vehicle is a driving abnormality.

[0091] Optionally, in this embodiment, when determining the abnormal information of the target vehicle according to the task information in the vehicle information, the cloud first determines the task status of the target vehicle according to the task information, and then determines whether the target vehicle has a task abnormality based on the task status. Further, the abnormal task status in this embodiment is used to characterize that the vehicle task execution goes wrong. The cloud determines whether the target vehicle has a task abnormality by comparing the current actual task status of the target vehicle with the preset task status, and determines that the target vehicle has a task abnormality when the current actual task status is inconsistent with the preset task status (for example, when the target vehicle needs to park in a specified parking spot but the current actual task status indicates that the current position of the target vehicle is far from the specified parking spot or parks in other non-specified parking spots). At this time, in response to the abnormal task status, the cloud determines that the abnormal situation of the target vehicle is a task abnormality.

[0092] It should be noted that the above abnormal situations refer to the abnormalities that the driverless autonomous vehicle can spontaneously detect and the abnormalities that the cloud can determine by analyzing the vehicle information, collectively referred to as the abnormalities that the system can detect. However, in actual situations, there may also be some scenarios that require the issuance of an MRC (Minimal Risk Condition) instruction, resulting in abnormal situations that cannot be determined based on vehicle information, such as the environment exceeding the ODD (Operational Design Domain), accident handling, and sensor fouling (also known as system-undetectable abnormalities). At this time, it is necessary to combine other detection methods (such as image acquisition, target detection methods, etc.) or manual judgment to discover the abnormalities, and add the system-undetectable abnormal situations and the corresponding abnormal judgment and processing logic to the control method of the fully driverless autonomous vehicle to enrich the coverage of abnormal situations, so that subsequent abnormal monitoring can predict and prevent future abnormal problems based on historical data and experience, and achieve the adaptive control of the driverless autonomous vehicle.

[0093] In step S400, a warning is given for the abnormal situation.

[0094] In this embodiment, when an abnormality occurs in the target vehicle, in order to facilitate the cloud personnel to timely discover and understand the vehicle abnormality and make corresponding handling operations for different abnormalities, a warning will be given for the abnormal situation.

[0095] Optionally, in this embodiment, methods such as text display and voice broadcast can be used to give a warning for the abnormal situation, and the same or different methods can be used for different abnormal situations. Further, in this embodiment, a warning is given by displaying warning information on the display interface in the cloud, and the content of the warning information includes the abnormal situation, and the abnormal situation can be displayed by rendering methods such as text bubbles and pop-up windows.

[0096] Optionally, since different abnormal situations have different degrees of impact on the driverless autonomous vehicle, in this embodiment, corresponding abnormal levels will be set for different abnormal situations. For example, the abnormal levels corresponding to the abnormal situations that affect safety, affect order processing, and require attention (such as those that have no impact on the current task processing but will affect the subsequent task processing process) are set as the first level, the second level, and the third level respectively. The earlier the abnormal level, the greater the impact on the driverless autonomous vehicle and the higher the warning level. At the same time, by embedding the abnormal level corresponding to each abnormal situation into the abnormal information, it is possible to determine both the abnormal situation and the abnormal level corresponding to the abnormal situation when the cloud determines the abnormal information of the target vehicle.

[0097] Further, in this embodiment, when warning of abnormal situations of the target vehicle, the abnormal situations are warned based on the abnormal levels of the abnormal situations, and different abnormal situations with different abnormal levels are displayed in different ways. For example, abnormal situations with a higher abnormal level are displayed with a darker color, more display times, and a shorter display length, etc. The display content may include information such as the abnormal situation, the time, location, and vehicle identifier when the abnormal situation occurs. Optionally, in this embodiment, when warning based on the abnormal level of the abnormal situation, the abnormal level is compared with a preset level, and different display methods are used for abnormal situations whose abnormal levels reach the preset level and those that do not reach the preset level. Specifically, when there are multiple abnormal levels at the same time, each of the abnormal situations whose abnormal levels reach the preset level is separately displayed on the display interface, and the abnormal situations whose abnormal levels do not reach the preset level are folded and displayed on the display interface. Thus, by using different display methods to distinguish and display abnormal situations of different levels, it is convenient for the cloud to quickly screen abnormal situations of different impact degrees, so as to prioritize the handling of urgent abnormalities, and while ensuring driving safety, it can further improve the reliability of the driverless autonomous vehicle.

[0098] Figure 4 is another schematic diagram of the display interface of the embodiment of the present invention. As Figure 4 shown, when an abnormal situation occurs in the target vehicle, the display interface in this embodiment further displays a warning pop-up window 38 on the basis of the display interface shown in Figure 3 the figure, and warning information is displayed through the warning pop-up window 38, so that the cloud can warn of the abnormal information in the target vehicle. Optionally, the warning information displayed in the warning pop-up window 38 in this embodiment includes content such as the warning level, the target vehicle identifier, the abnormal situation identifier, the time when the abnormal situation occurs, and the countermeasures for the abnormal situation, and the warning level is consistent with the abnormal level of the abnormal situation. At the same time, when there are warning information of multiple abnormal situations, in this embodiment, the warning information of the high-level abnormal situations is separately displayed, and the warning information of the low-level abnormal situations is folded and displayed, with the most recent one displayed on the top layer. Thus, in this embodiment, by displaying warning information on the display interface, it is convenient for the cloud personnel to timely understand the abnormal information existing in the target vehicle, and quickly take corresponding solutions based on the type and abnormal level of the abnormal situation, thereby being able to avoid the danger and vehicle damage caused by vehicle abnormalities, and improving the safety and reliability of vehicle driving.

[0099] Figure 5 is the warning strategy table of the abnormal situation of the embodiment of the present invention. As Figure 5The warning strategy table shown shows different levels of abnormal situations and the corresponding judgment methods and warning contents for each abnormal situation. The specific judgment method and display method have been introduced above and will not be repeated here. Furthermore, in order to reduce the interference of continuous warnings on the vehicle control process, the cloud in this embodiment will adopt a certain disappearance strategy to end the warning. Optionally, Figure 5 As shown, the disappearance strategy in this embodiment includes adopting a disappearance strategy based on abnormality detection, ending the warning after the self-service detection shows that the abnormality disappears; or adopting a countdown disappearance strategy, by setting the warning display time (e.g., 3 seconds), and ending the warning after the warning display time. Correspondingly, the control method in this embodiment also includes: ending the warning in response to the disappearance of the abnormal situation; or ending the warning in response to the warning information display time exceeding the preset time.

[0100] In step S500, a remote control instruction is issued to the target vehicle according to the abnormal situation, so that when the target vehicle cannot resolve the abnormality through the backup system taking over, a corresponding operation is performed based on the remote control instruction.

[0101] In this embodiment, since the target vehicle has a certain ability to handle exceptions, when an abnormal situation occurs in the target vehicle, the target vehicle's backup system is prioritized to resolve the exception. When the target vehicle cannot resolve the exception through the backup system, the corresponding operation is performed based on the remote control instruction. Specifically, the remote control instruction is sent to the target vehicle through the cloud so that the target vehicle executes the operation corresponding to the remote control instruction. Optionally, the remote control instruction in this embodiment is used to control including parking, going offline, and waiting for rescue. Among them, the parking instruction is used to control the target vehicle to stop immediately or park in a designated parking spot or the parking spot closest to the target vehicle. The offline instruction is used to control the target vehicle to stop accepting orders. The waiting for rescue instruction is used to control the target vehicle to stop driving and wait for offline rescue.

[0102] Optionally, in this embodiment, the cloud sends corresponding remote control instructions to the target vehicle for different abnormal situations. Specifically, when a system abnormality occurs in the target vehicle, if the backup system of the target vehicle cannot be taken over or the backup system cannot eliminate the system abnormality after taking over, it is necessary to take manual takeover measures for the target vehicle; at this time, the cloud sends a remote control instruction to the target vehicle to make the target vehicle stop and wait for manual takeover on the spot. When the target vehicle has a communication abnormality in the operation and maintenance abnormality, the cloud sends a remote control instruction to the target vehicle to control the target vehicle to re-establish a connection with the cloud. If the re-establishment of the connection is successful, the abnormality is eliminated, otherwise the vehicle is controlled to stop and wait for manual takeover at the origin. When the target vehicle has an endurance abnormality in the operation and maintenance abnormality, the cloud sends a remote control instruction to the target vehicle to control the target vehicle to pull over or drive into the nearest gas station to refuel. When the target vehicle has a driving abnormality or a mission abnormality, the cloud will send a remote control instruction to the target vehicle to control the target vehicle to pull over and wait for offline rescue. Therefore, by using the above method to remotely control the target vehicle where an abnormal situation occurs, the driving safety and reliability of the target vehicle can be improved while speeding up the abnormal situation resolution process and improving the efficiency of abnormality handling.

[0103] It should be understood that in this embodiment, step S400 and step S500 may be executed simultaneously, or step S400 may be executed first and then step S500. The execution order of the above two steps is not limited here.

[0104] The technical solution of the embodiment of the present invention dispatches tasks to at least one unmanned autonomous driving vehicle according to a preset shift schedule, obtains the vehicle information of the target vehicle within the preset shift schedule, determines the abnormal information of the target vehicle according to the vehicle information, and issues an early warning for the abnormal situation corresponding to the abnormal information; sends a remote control instruction to the target vehicle according to the abnormal situation, so that the target vehicle can perform corresponding operations based on the remote control instruction when the target vehicle cannot resolve the abnormality through the backup system. Therefore, by monitoring the performance of the unmanned autonomous driving vehicle's shift schedule, vehicle abnormalities can be discovered and resolved in a timely manner, thereby improving the safety and reliability of the unmanned autonomous driving vehicle.

[0105] Figure 6 Schematic diagram of a control system for a fully unmanned autonomous driving vehicle according to an embodiment of the present invention. Figure 6As shown, the control system in this embodiment includes a task module 1, a monitoring module 2, an alarm module 3 and a remote control module 4. Among them, the task module 1 is used to dispatch tasks to at least one unmanned automatic driving vehicle according to the preset scheduling time, and the tasks include road test tasks and order tasks. The monitoring module 2 is used to obtain the vehicle information of the target vehicle within the preset scheduling time. The alarm module 3 is used to determine the abnormal information of the target vehicle according to the vehicle information, and the abnormal information includes abnormal situations; and, it is used to warn of abnormal situations. The remote control module 4 is used to issue remote control instructions to the target vehicle according to the abnormal situation, so as to perform corresponding operations based on the remote control instructions when the vehicle cannot be taken over by the backup system to eliminate the abnormality. Optionally, the remote control instructions in this embodiment are used to control including parking, offline and waiting for rescue. Among them, the parking instruction is used to control the target vehicle to stop immediately or park in a designated parking spot or the parking spot closest to the target vehicle. The offline instruction is used to control the target vehicle to stop accepting orders. The waiting for rescue instruction is used to control the target vehicle to stop driving and wait for offline rescue.

[0106] Optionally, the task module 1 in this embodiment is also used to control the target vehicle to start executing the task in response to the target vehicle successfully receiving the order; and to control the display of the task details of the target vehicle on the display interface. The task details include task attributes and status information, and the task attributes include one or more of the task name, task execution time, task start point and task end point. The status information is used to characterize the task execution status of the vehicle, and the task execution status includes receiving the order, picking up the driver, waiting, delivering the driver, completed and / or idle, etc.

[0107] Furthermore, a cancel control is also displayed on the display interface, and the remote control module 4 is also used to control the target vehicle to go offline or stop in response to the cancel control being triggered; and in response to the end of the preset scheduling time and the target vehicle having unfinished tasks, control the target vehicle to go offline or stop after all tasks are completed.

[0108] Optionally, the vehicle information in this embodiment includes one or more of the fault detection results, operation and maintenance information, driving information and task information of the intelligent driving system. Further, when the abnormal information of the target vehicle is determined according to the vehicle information and the vehicle information includes the fault detection result, the monitoring module 2 is used to respond to the fault detection result indicating the occurrence of a system abnormality, and determine that the abnormal situation of the target vehicle is a system abnormality, and the system abnormality is used to indicate a hardware abnormality or a software abnormality.

[0109] Further, when determining the abnormal information of the target vehicle based on the vehicle information and the vehicle information includes operation and maintenance information, the monitoring module 2 is used to judge the operation and maintenance status of the target vehicle according to the operation and maintenance information; and in response to an abnormal operation and maintenance status, determine that the abnormal situation of the target vehicle is an operation and maintenance abnormality. Wherein, the operation and maintenance status is a communication connection status or a sustainable cruising status, and an abnormal operation and maintenance status is used to indicate that the vehicle is out of contact or the vehicle is not sustainable.

[0110] Further, when determining the abnormal information of the target vehicle based on the vehicle information and the vehicle information includes driving information, the monitoring module 2 is used to judge the driving status of the target vehicle according to the driving information; and in response to an abnormal driving status, determine that the abnormal situation of the target vehicle is a driving abnormality. Wherein, an abnormal driving status is used to indicate that the vehicle driving pauses or is blocked.

[0111] Further, when determining the abnormal information of the target vehicle based on the vehicle information and the vehicle information includes task information, the monitoring module 2 is used to determine the task status of the target vehicle according to the task information; and in response to an abnormal task status, determine that the abnormal situation of the target vehicle is a task abnormality. Wherein, an abnormal task status is used to indicate that an error occurs in the vehicle task execution.

[0112] Optionally, in addition to the abnormal situation, the abnormal information in this embodiment further includes an abnormal level. When warning about the abnormal situation, the alarm module 3 is specifically used to warn about the abnormal situation based on the abnormal level of the abnormal situation. Further, the alarm module 3 is further used to respectively display on the display interface each abnormal situation whose abnormal level reaches the preset level; fold and display on the display interface each abnormal situation whose abnormal level does not reach the preset level; and, in response to the disappearance of the abnormal situation, end the warning.

[0113] Figure 7 is a schematic diagram of the electronic device according to the embodiment of the present invention. As Figure 7 shown, Figure 7The electronic device shown is a general address query device, which includes a general computer hardware structure, and at least includes a processor 71 and a memory 72. The processor 71 and the memory 72 are connected by a bus 73. The memory 72 is adapted to store instructions or programs executable by the processor 71. The processor 71 can be an independent microprocessor or a set of one or more microprocessors. Thus, by executing the instructions stored in the memory 72, the processor 71 executes the method flow of the embodiment of the present invention as described above to implement data processing and control of other devices. The bus 73 connects the above-mentioned multiple components together and also connects the above-mentioned components to a display controller 74, a display device, and an input / output (I / O) device 75. The input / output (I / O) device 75 can be a mouse, a keyboard, a modem, a network interface, a touch input device, a body-sensing input device, a printer, and other devices well-known in the art. Typically, the input / output device 75 is connected to the system through an input / output (I / O) controller 76.

[0114] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a device (equipment), or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can be implemented as a computer program product on one or more computer-readable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code.

[0115] The present application is described with reference to the flowcharts of methods, devices (equipment), and computer program products according to the embodiments of the present application. It should be understood that each process in the flowchart can be implemented by computer program instructions. These computer program instructions can be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured article including an instruction device that implements the Figure 1 functions specified in one process or multiple processes.

[0116] These computer program instructions can also be provided to the processor of a general computer, a special computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices produce a device for implementing the Figure 1 functions specified in one process or multiple processes.

[0117] Another embodiment of the present invention relates to a non-volatile storage medium for storing a computer-readable program, and the computer-readable program is used for a computer to execute the above-mentioned partial or all method embodiments.

[0118] That is, those skilled in the art can understand that all or part of the steps in implementing the methods of the above embodiments can be completed by specifying relevant hardware through a program. This program is stored in a storage medium and includes several instructions to enable a device (which can be a single-chip microcomputer, a chip, etc.) or a processor to execute all or part of the steps of the methods described in the embodiments of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0119] The above are only the preferred embodiments of the present application and are not used to limit the present application. For those skilled in the art, various modifications and changes can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A control method for a fully unmanned automatic driving vehicle, characterized in that: The method comprises: Dispatching tasks to at least one unmanned autonomous driving vehicle according to a preset schedule, wherein the tasks include road test tasks and order tasks; Obtaining vehicle information of the target vehicle within the preset scheduling time; Determining abnormal information of the target vehicle according to the vehicle information, wherein the abnormal information includes an abnormal situation; Providing early warning of the abnormal situation; A remote control instruction is issued to the target vehicle according to the abnormal situation, so that a corresponding operation is performed based on the remote control instruction when the target vehicle cannot resolve the abnormality through the backup system taking over.

2. The method according to claim 1, characterized in that: The method further comprises: The task details of the target vehicle are displayed on the display interface, and the task details include task attributes and status information. The task attributes include one or more of the task name, task execution time, task starting point and task end point. The status information is used to characterize the task execution status of the vehicle, and the task execution status includes receiving orders, picking up, waiting, delivering, completed or idle.

3. The method according to claim 2, characterized in that The display interface also displays a cancel control, and the method further includes: In response to the cancellation control being triggered, the target vehicle is controlled to go offline or stop.

4. The method according to claim 2, characterized in that: The method further comprises: In response to the target vehicle successfully receiving the order, the target vehicle is controlled to start executing the task.

5. The method according to claim 2, characterized in that: The method further comprises: In response to the end of the preset scheduling time and the target vehicle having unfinished tasks, the target vehicle is controlled to go offline or stop after all tasks are completed.

6. The method according to claim 1, characterized in that The abnormal information also includes an abnormal level, and the early warning of the abnormal situation includes: An early warning is issued for the abnormal situation based on the abnormality level of the abnormal situation.

7. The method according to claim 6, characterized in that The issuing of an early warning for the abnormal situation based on the abnormal level of the abnormal situation includes: Displaying each of the abnormal situations whose abnormality level reaches a preset level on the display interface; On the display interface, each of the abnormal situations whose abnormal level does not reach the preset level is folded and displayed.

8. The method according to claim 1, characterized in that The method further comprises: In response to the abnormal situation disappearing, the early warning is ended.

9. The method according to claim 1, characterized in that: The vehicle information includes a fault detection result of the intelligent driving system, and the abnormal information of the target vehicle determined according to the vehicle information includes: In response to the fault detection result indicating a system abnormality, the abnormal condition of the target vehicle is determined to be a system abnormality, where the system abnormality is used to indicate a hardware abnormality or a software abnormality.

10. The method according to claim 1, characterized in that The vehicle information includes operation and maintenance information, and determining the abnormal information of the target vehicle according to the vehicle information includes: Determining the operation and maintenance state of the target vehicle according to the operation and maintenance information, wherein the operation and maintenance state is a communication connection state or a sustainable cruising state; In response to an abnormality in the operation and maintenance status, it is determined that the abnormal situation of the target vehicle is an operation and maintenance abnormality, and the abnormality in the operation and maintenance status is used to indicate that the vehicle is out of contact or the vehicle cannot continue to travel.

11. The method according to claim 1, characterized in that: The vehicle information includes driving information, and determining the abnormal information of the target vehicle according to the vehicle information includes: Determining the driving state of the target vehicle according to the driving information; In response to the abnormality in the driving state, it is determined that the abnormal situation of the target vehicle is a driving abnormality, and the abnormality in the driving state is used to indicate that the vehicle is stopped or blocked.

12. The method according to claim 1, characterized in that The vehicle information includes task information, and determining the abnormal information of the target vehicle according to the vehicle information includes: Determine the mission status of the target vehicle according to the mission information; In response to the abnormality of the task status, the abnormal situation of the target vehicle is determined to be a task abnormality, and the abnormality of the task status is used to indicate that the vehicle task execution is wrong.

13. A control system for a fully unmanned automatic driving vehicle, characterized in that: The system comprises: A task module, used to dispatch tasks to at least one unmanned autonomous driving vehicle according to a preset scheduling time, wherein the tasks include road test tasks and order tasks; A monitoring module, used to obtain vehicle information of a target vehicle within the preset scheduling time; An alarm module, used to determine abnormal information of the target vehicle according to the vehicle information, wherein the abnormal information includes an abnormal situation; and to issue an early warning for the abnormal situation; The remote control module is used to send a remote control instruction to the target vehicle according to the abnormal situation, so as to perform a corresponding operation based on the remote control instruction when the vehicle cannot resolve the abnormality through the backup system.

14. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method according to any one of claims 1 to 12.

15. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method steps of any one of claims 1 to 12 are implemented.

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