Remote assisted driving control method, device, storage medium and program product

Abstract

Embodiments of the present application provide a remote assisted driving control method, device, storage medium and program product. On the basis of obtaining the driving image information, driving time and planning driving route information sent by the image acquisition unit of each vehicle, the environmental complexity index of the corresponding vehicle is obtained according to the driving route information and the driving image information, the time complexity index of the corresponding vehicle is obtained according to the driving time, and then the comprehensive complexity index of each vehicle is obtained. In combination with the bearing threshold of the remote assisted driving personnel of the remote assistance end, the target number of the corresponding controllable vehicle and the target vehicle information are determined. Then, the target vehicle information is sent to the remote assistance end based on the target number, so that the remote assistance end displays the real-time driving image of the target number of vehicles on the display unit, the remote assisted driving personnel remotely assists the corresponding controllable vehicle, and the problem of high operation and maintenance cost of unmanned vehicles is solved.

Description

Technical Field

[0001] This application relates to the field of remote assisted driving technology, and in particular to a remote assisted driving control method, device, storage medium and program product. Background Technology

[0002] With the continuous development of autonomous driving technology, driverless vehicles based on autonomous driving technology are widely used in the vehicle transportation industry; during the operation of driverless vehicles, driving safety must be ensured first.

[0003] In existing technologies, in order to ensure the driving safety of autonomous vehicles, it is common practice to assign a one-to-one on-site personnel to each autonomous vehicle so that in the event of an emergency, the autonomous vehicle can be manually taken over and controlled before an accident occurs, thereby preventing an accident from happening.

[0004] However, existing technological solutions have led to high operating and maintenance costs for autonomous vehicles. Summary of the Invention

[0005] This application provides a remote assisted driving control method, device, storage medium, and program product to solve the problem of high operation and maintenance costs of unmanned vehicles.

[0006] In a first aspect, embodiments of this application provide a remote assisted driving control method applied to a server, comprising: receiving an image acquisition command sent by a remote assistance terminal; sending the image acquisition command to image acquisition units of multiple vehicles to be controlled, so that each vehicle's image acquisition unit acquires driving image information, driving time corresponding to the driving image information, and planned driving route information according to the image acquisition command; receiving the driving image information, driving time, and planned driving route information sent by the image acquisition unit of each vehicle; performing environmental complexity analysis processing on the planned driving route information and the driving image information for each vehicle to obtain an environmental complexity index for the corresponding vehicle, wherein the environmental complexity index characterizes the complexity of the vehicle's driving environment; and performing time complexity analysis on each vehicle based on each driving time. The system processes and obtains the time complexity index of the corresponding vehicle, where the time complexity characterizes the probability of the vehicle causing an accident at different times. Based on the environmental complexity index and the time complexity index, a comprehensive complexity index for each vehicle is determined. A carrying threshold for the comprehensive complexity index of the remote assisted driver is obtained. Based on the carrying threshold and the comprehensive complexity index of each vehicle, a target number of vehicles controllable by the remote assisted driver and target vehicle information are determined from the plurality of vehicles to be controlled. The target number of controllable vehicles and target vehicle information are sent to the remote assisted driver so that the remote assisted driver displays real-time driving images of the target number of vehicles on a display unit. The real-time driving images are used to instruct the remote assisted driver to perform remote assisted driving on the corresponding controllable vehicles.

[0007] In one possible implementation, the step of performing environmental complexity analysis processing on the planned driving route information and the driving image information to obtain the corresponding vehicle's environmental complexity index includes: generating an environmental complexity correction coefficient and an estimated environmental complexity index based on the planned driving route information; performing environmental complexity analysis processing on the driving image information to obtain a real-time environmental complexity index; comparing the magnitude relationship between the real-time environmental complexity index and the estimated environmental complexity index; if the real-time environmental complexity index is greater than or equal to the estimated environmental complexity index, then determining the real-time environmental complexity index as the environmental complexity index; if the real-time environmental complexity index is less than the estimated environmental complexity index, then obtaining the environmental complexity index based on the correction coefficient and the real-time environmental complexity index.

[0008] In one possible implementation, generating an environmental complexity correction coefficient and an estimated environmental complexity index based on the planned driving route information includes: updating the planned driving route information in real time to obtain real-time planned driving route information; and generating the correction coefficient and the estimated environmental complexity index based on the real-time planned driving route information.

[0009] In one possible implementation, after sending the target number of controllable vehicles and the target vehicle information to the remote assistance terminal, the method further includes: calculating the remote assisted driving duration of the remote assisted driver based on the driving time of the controllable vehicles to obtain the remote assisted driving duration; updating the target number based on the remote assisted driving duration to obtain the updated target number; determining the corresponding updated target vehicle information among the plurality of vehicles to be controlled based on the updated target number; and sending the updated target number and the updated target vehicle information to the remote assistance terminal so that the remote assistance terminal displays the real-time driving image of the updated target number of vehicles on the display unit.

[0010] In one possible implementation, sending the image acquisition command to the image acquisition units of multiple vehicles to be controlled includes: sending the image acquisition command to the image acquisition units of multiple vehicles to be controlled via the MQTT communication protocol.

[0011] In one possible implementation, receiving the driving image information sent by the image acquisition unit of each vehicle includes: receiving a compressed encoded data stream sent by the image acquisition unit of each vehicle through a public cloud RTC service; wherein the compressed encoded data stream is obtained by the image acquisition unit compressing and encoding the driving image information; and decoding the compressed encoded data stream to obtain the driving image information.

[0012] In one possible implementation, after sending the target number of controllable vehicles and target vehicle information to the remote assistance terminal, the method further includes: receiving a remote assistance command sent by the remote assistance terminal based on a public cloud RTM service; the remote assistance command is generated by the remote assistance terminal in response to a triggering operation by a remote assisted driver based on the vehicle driving image; and sending the remote assistance command to the corresponding target vehicle based on the public cloud RTM service, so that the target vehicle drives or stops according to the remote assistance command.

[0013] Secondly, embodiments of this application provide a remote assisted driving control device, applied to a server, comprising:

[0014] The receiving module is used to receive image acquisition commands sent by the remote auxiliary terminal;

[0015] The sending module is used to send the image acquisition command to the image acquisition units of multiple vehicles to be controlled, so that the image acquisition unit of each vehicle can acquire driving image information, driving time corresponding to the driving image information, and planned driving route information according to the image acquisition command;

[0016] The receiving module is used to receive driving image information, driving time, and planned driving route information sent by the image acquisition unit of each vehicle.

[0017] The processing module is used to perform environmental complexity analysis processing on the planned driving route information and the driving image information for each vehicle to obtain the environmental complexity index of the corresponding vehicle, wherein the environmental complexity index characterizes the complexity of the vehicle's driving environment.

[0018] The processing module is used to perform time complexity analysis processing on each vehicle according to each driving time to obtain the time complexity index of the corresponding vehicle, wherein the time complexity represents the probability of the vehicle having an accident at different times.

[0019] The processing module is used to determine the comprehensive complexity index of each vehicle based on the environmental complexity index and the time complexity index.

[0020] The acquisition module is used to acquire the carrying threshold of the comprehensive complexity index of the remote assisted driving personnel of the remote assistance terminal;

[0021] The processing module is used to determine the target number of vehicles that the remote assisted driving personnel can control and the target vehicle information among the multiple vehicles to be controlled, based on the load threshold and the comprehensive complexity index of each vehicle.

[0022] The sending module is used to send the target number of controllable vehicles and target vehicle information to the remote assistance terminal, so that the remote assistance terminal displays real-time driving images of the target number of vehicles on the display unit. The real-time driving images of the vehicles are used to instruct the remote assisted driver to perform remote assisted driving on the corresponding controllable vehicles.

[0023] In one possible implementation, when the processing module performs environmental complexity analysis on the planned driving route information and the driving image information to obtain the environmental complexity index of the corresponding vehicle, it specifically performs the following: generating an environmental complexity correction coefficient and an estimated environmental complexity index based on the planned driving route information; performing environmental complexity analysis on the driving image information to obtain a real-time environmental complexity index; comparing the magnitude of the real-time environmental complexity index and the estimated environmental complexity index; if the real-time environmental complexity index is greater than or equal to the estimated environmental complexity index, then determining the real-time environmental complexity index as the environmental complexity index; if the real-time environmental complexity index is less than the estimated environmental complexity index, then obtaining the environmental complexity index based on the correction coefficient and the real-time environmental complexity index.

[0024] In one possible implementation, when the processing module generates the environmental complexity correction coefficient and the estimated environmental complexity index based on the planned driving route information, it is specifically used to: update the planned driving route information in real time to obtain real-time planned driving route information; and generate the correction coefficient and the estimated environmental complexity index based on the real-time planned driving route information.

[0025] In one possible implementation, after sending the target number of controllable vehicles and the target vehicle information to the remote assistance terminal, the remote assisted driving control device is further configured to: calculate the remote assisted driving duration of the remote assisted driver based on the driving time of the controllable vehicles, to obtain the remote assisted driving duration; update the target number based on the remote assisted driving duration, to obtain the updated target number; determine the corresponding updated target vehicle information among the plurality of vehicles to be controlled based on the updated target number; and send the updated target number and the updated target vehicle information to the remote assistance terminal, so that the remote assistance terminal displays the real-time driving image of the updated target number of vehicles on the display unit.

[0026] In one possible implementation, when the sending module sends the image acquisition command to the image acquisition units of multiple vehicles to be controlled, it is specifically used to: send the image acquisition command to the image acquisition units of multiple vehicles to be controlled via the MQTT communication protocol.

[0027] In one possible implementation, when the receiving module receives driving image information sent by the image acquisition unit of each vehicle, it is specifically used to: receive a compressed encoded data stream sent by the image acquisition unit of each vehicle through a public cloud RTC service; wherein the compressed encoded data stream is obtained by the image acquisition unit compressing and encoding the driving image information; and decode the compressed encoded data stream to obtain the driving image information.

[0028] In one possible implementation, after sending the target number of controllable vehicles and target vehicle information to the remote assistance terminal, the remote assisted driving control device is further configured to: receive remote assistance commands sent by the remote assistance terminal based on a public cloud RTM service; the remote assistance commands are generated by the remote assistance terminal in response to a triggering operation by a remote assisted driver based on the vehicle driving image; and send the remote assistance commands to the corresponding target vehicles based on the public cloud RTM service, so that the target vehicles drive or stop according to the remote assistance commands.

[0029] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0030] The memory stores computer-executed instructions;

[0031] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0032] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0033] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0034] The remote assisted driving control method, device, storage medium, and program product provided in this application embodiment receive image acquisition instructions sent by a remote assistance terminal; send the image acquisition instructions to image acquisition units of multiple vehicles to be controlled, so that each vehicle's image acquisition unit acquires driving image information, the driving time corresponding to the driving image information, and planned driving route information according to the image acquisition instructions; receives the driving image information, driving time, and planned driving route information sent by each vehicle's image acquisition unit; for each vehicle, performs environmental complexity analysis processing on the planned driving route information and the driving image information to obtain the corresponding vehicle's environmental complexity index, wherein the environmental complexity index characterizes the complexity of the vehicle's driving environment; for each vehicle, performs time complexity analysis processing based on each driving time. The system obtains the time complexity index of the corresponding vehicle, where the time complexity represents the probability of the vehicle causing an accident at different times; based on the environmental complexity index and the time complexity index, it determines the comprehensive complexity index of each vehicle; it obtains the carrying threshold of the comprehensive complexity index of the remote assisted driver at the remote assistance terminal; based on the carrying threshold and the comprehensive complexity index of each vehicle, it determines the target number of vehicles that the remote assisted driver can control and the target vehicle information among the multiple vehicles to be controlled; it sends the target number of controllable vehicles and the target vehicle information to the remote assistance terminal, so that the remote assistance terminal displays the real-time driving images of the target number of vehicles on the display unit, and the real-time driving images of the vehicles are used to instruct the remote assisted driver to perform remote assisted driving on the corresponding controllable vehicles. By receiving and sending image acquisition commands to obtain driving image information, driving time, and planned driving route information sent by the image acquisition unit of each vehicle, the environmental complexity index of the corresponding vehicle is obtained based on the driving route information and driving image information, and the time complexity index of the corresponding vehicle is obtained based on the driving time, thus obtaining the comprehensive complexity index of each vehicle. Furthermore, combined with the carrying threshold of the comprehensive complexity index of the remote assisted driver at the remote assistance terminal, the target number of controllable vehicles and the target vehicle information are determined. Then, the server sends the target vehicle information to the remote assistance terminal based on the target number, so that the remote assistance terminal displays the real-time driving images of the target number of vehicles on the display unit, thereby enabling the remote assisted driver to remotely assist driving the corresponding (one or more) controllable vehicles, solving the problem of high operation and maintenance costs of unmanned vehicles caused by existing technical solutions. Attached Figure Description

[0035] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0036] Figure 1 A schematic diagram illustrating a scenario for the remote assisted driving control method provided in this application;

[0037] Figure 2 A flowchart illustrating a remote assisted driving control method provided in one embodiment of this application;

[0038] Figure 3 for Figure 2 A schematic diagram illustrating the specific implementation steps of step S104 in the illustrated embodiment;

[0039] Figure 4 for Figure 3 A schematic diagram illustrating the specific implementation steps of step S1041 in the illustrated embodiment;

[0040] Figure 5 A schematic diagram illustrating the determination of a target number of controllable vehicles, provided in an embodiment of this application;

[0041] Figure 6 This is a schematic diagram of the structure of a remote assisted driving control device provided in one embodiment of this application;

[0042] Figure 7 A schematic diagram of the structure of the electronic device provided in this application.

[0043] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0044] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0045] The technical solution of this application involves the collection, storage, use, processing, transmission, provision and disclosure of user personal information and data, which comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0046] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0047] First, let me explain the terms used in this application:

[0048] MQTT communication protocol: Message Queuing Telemetry Transport. It is a message protocol based on the publish / subscribe paradigm. It works on top of the TCP / IP protocol suite and is designed for remote devices with low hardware performance and environments with poor network conditions.

[0049] Public Cloud RTC Service: Real-Time Communication (RTC) is a real-time audio and video communication solution based on the public cloud. It provides low latency, high concurrency, and high-definition, smooth audio and video communication capabilities.

[0050] Public cloud RTM service: Real-Time Messaging, or RTM for short, is a real-time communication service based on a public cloud platform. It provides low-latency, high-reliability message delivery and real-time data transmission capabilities.

[0051] The application scenarios of the embodiments of this application are explained below:

[0052] Figure 1 This is a schematic diagram of a scenario for the remote assisted driving control method provided in this application, such as... Figure 1As shown, the specific application scenario of this application is to provide remote assisted driving services to unmanned vehicles based on autonomous driving technology. The execution subject of the method provided in this application embodiment can be an electronic control unit, a terminal device, or a server. Taking the server as the execution subject, during the operation of the unmanned vehicle, the first priority is to ensure the driving safety of the vehicle. Based on the method provided in this application, the server acquires driving image information collected by the image acquisition units of multiple vehicles to be controlled, the driving time corresponding to the driving image information, and the planned driving route information; generates a target number of controllable vehicles and target vehicle information that matches the remote assisted driving personnel at the remote assistance end; and then displays the real-time driving images of the target number of vehicles on the display unit of the remote assistance end to realize remote assisted driving. The driver remotely assists driving a corresponding number of controllable vehicles. Exemplarily, the server receives driving image information 1 from the image acquisition unit of vehicle car_1 and sends it to the remote assistance terminal. The display unit of the remote assistance terminal displays driving image information 1 (video source 1) and driving image information 2 (video source 2) of vehicle car_2. Furthermore, when the remote-assisted driver detects an obstacle in front of vehicle car_1 through the display unit, the remote assistance terminal responds to the driver's triggering operation based on the obstacle in the vehicle's driving image by generating a remote assistance command. The server then receives the remote assistance command sent by the remote assistance terminal and sends it to the target vehicle, causing the target vehicle to move or stop according to the remote assistance command, thereby ensuring vehicle driving safety.

[0053] In existing technologies, to ensure the safety of autonomous vehicles, a one-to-one on-site human operator is typically assigned to each vehicle. This allows for manual takeover and control of the autonomous vehicle in emergencies, before an accident occurs, thus preventing a potential incident. However, this existing approach results in high operating and maintenance costs for autonomous vehicles.

[0054] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0055] Figure 2 A flowchart of a remote assisted driving control method provided in one embodiment of this application is shown below. Figure 2As shown, the execution subject of the remote assisted driving control method provided in this embodiment can be an electronic control unit, a terminal device, or a server. For example, this embodiment uses a server as the execution subject for explanation. The remote assisted driving control method provided in this embodiment includes the following steps:

[0056] Step S101: Receive the image acquisition command sent by the remote auxiliary terminal.

[0057] Step S102: The image acquisition command is sent to the image acquisition units of multiple vehicles to be controlled, so that the image acquisition unit of each vehicle can acquire driving image information, driving time corresponding to the driving image information, and planned driving route information according to the image acquisition command.

[0058] For example, the planned driving route information is used to instruct the vehicle to be controlled to follow the planned driving route based on the origin and destination. Then, the server receives an image acquisition command sent by a remote auxiliary terminal, and then sends the image acquisition command to the image acquisition units of multiple vehicles to be controlled, so that each vehicle's image acquisition unit acquires driving image information, the corresponding driving time, and the planned driving route information according to the image acquisition command; wherein, the driving time corresponds to the video frames in the driving image information. Further, for each vehicle, this application does not specifically limit the number of videos acquired by its image acquisition unit. For example, if the vehicle has only one front-road image acquisition unit, then the number of videos acquired is 1, thus obtaining front-road driving image information; if the vehicle is equipped with one front-road image acquisition unit, one rear-road image acquisition unit, one left-road image acquisition unit, and one right-road image acquisition unit, then the number of videos acquired is 4, thus obtaining front-road driving image information, rear-road driving image information, left-road driving image information, and right-road driving image information. The vehicle to be controlled is... Figure 1 The scene shown illustrates an autonomous vehicle.

[0059] In one possible implementation, the server sends image acquisition commands to the image acquisition units of multiple vehicles to be controlled via the MQTT communication protocol; the method of sending image acquisition commands via the MQTT communication protocol ensures high reliability of command transmission.

[0060] Step S103: Receive driving image information, driving time, and planned driving route information sent by the image acquisition unit of each vehicle.

[0061] For example, after the image acquisition unit completes the acquisition of driving image information, driving time, and planned driving route information in response to the image acquisition command, the server receives the driving image information, driving time, and planned driving route information sent by the image acquisition unit of each vehicle in real time. Specifically, for example, the server receives the encoded data sent by the image acquisition unit of each vehicle based on the communication protocol, wherein the encoded data is obtained by the image acquisition unit encoding the driving image information, driving time, and planned driving route information. Then, after receiving the encoded data, the server decodes the encoded data to obtain the driving image information, driving time, and planned driving route information sent by the image acquisition unit of each vehicle.

[0062] In one possible implementation, the server receives compressed coded data streams from the image acquisition units of each vehicle via a public cloud RTC service. These compressed coded data streams are obtained by the image acquisition units compressing and encoding driving image information. The server then decodes these compressed coded data streams to obtain the driving image information. Furthermore, the server can also receive and decode the corresponding compressed coded data streams for driving time and planned driving route information via the public cloud RTC service.

[0063] Step S104: For each vehicle, perform environmental complexity analysis on the planned driving route information and driving image information to obtain the corresponding vehicle's environmental complexity index, where the environmental complexity index characterizes the complexity of the vehicle's driving environment.

[0064] For example, the environmental complexity index characterizes the complexity of the vehicle's driving environment. For each vehicle, the server performs environmental complexity analysis on its driving route information and driving image information to obtain the corresponding environmental complexity index. Specifically, taking any vehicle to be controlled as an example, such as an unmanned delivery vehicle, the server determines the location information of the corresponding destination based on the delivery task of the unmanned delivery vehicle, and then generates planned driving route information. The planned driving route information indicates the estimated driving environment complexity of the planned driving route. Further, the server quantifies the estimated driving environment complexity based on the planned driving route information to obtain the first environmental complexity index envi_1. Further, the server performs real-time environmental complexity analysis based on the driving image information, such as analyzing the vehicle flow, pedestrian flow around the vehicle, and road width and other environmental features in the driving image information to obtain the second environmental complexity index envi_2. Then, the server performs weighted calculation on the first environmental complexity index envi_1 and the second environmental complexity index envi_2 to obtain the environmental complexity index envi_0. The calculation formula is shown in equation (1).

[0065] (1)

[0066] Where a and b are weighting coefficients.

[0067] In another possible implementation, Figure 3 for Figure 2 The schematic diagram of the specific implementation steps of step S104 in the embodiment shown is as follows: Figure 3 As shown, the specific implementation steps of step S104 include:

[0068] Step S1041: Based on the planned driving route information, generate an environmental complexity correction coefficient and an estimated environmental complexity index.

[0069] Step S1042: Perform environmental complexity analysis on the driving image information to obtain a real-time environmental complexity index.

[0070] Step S1043: Compare the magnitudes of the real-time environmental complexity index and the estimated environmental complexity index.

[0071] Step S1044: If the real-time environmental complexity index is greater than or equal to the estimated environmental complexity index, then the real-time environmental complexity index is determined as the environmental complexity index.

[0072] Step S1045: If the real-time environmental complexity index is less than the estimated environmental complexity index, then the environmental complexity index is obtained based on the correction coefficient and the real-time environmental complexity index.

[0073] For example, the server quantifies the estimated driving environment complexity based on the planned driving route information, generating an estimated environment complexity index EN_1 and a corresponding environment complexity correction coefficient x. Further, the server performs real-time environment complexity analysis based on driving image information, such as analyzing environmental features like vehicle traffic flow, pedestrian traffic around vehicles, and road width, to obtain a real-time environment complexity index EN_2. Then, the server compares the estimated environment complexity index EN_1 and the estimated environment complexity index EN_2. If the real-time environment complexity index EN_2 is greater than or equal to the estimated environment complexity index EN_1, it indicates that the estimated environment complexity obtained by the server based on the planned driving route information is... The real-time environmental complexity index EN_2 is lower than the real-time environmental complexity index EN_0 determined based on driving image information. Therefore, based on the basic principle of ensuring driving safety, the real-time environmental complexity index EN_2 is determined as the environmental complexity index EN_0. If the real-time environmental complexity index EN_2 is less than the estimated environmental complexity index EN_1, it indicates that the estimated environmental complexity obtained by the server based on the planned driving route information is higher than the real-time environmental complexity determined based on driving image information. Therefore, based on the basic principle of ensuring driving safety, and in order to prevent the "target number of vehicles that can be controlled by remote assisted driving personnel" determined in subsequent steps from changing frequently, the real-time environmental complexity index EN_2 is corrected according to the correction coefficient x to obtain the environmental complexity index EN_0. The calculation formula for the correction is shown in equation (2).

[0074] (2)

[0075] Furthermore, in yet another possible implementation, Figure 4 for Figure 3 A schematic diagram of the specific implementation steps of step S1041 in the illustrated embodiment is shown below. Figure 4 As shown, the specific implementation steps of step S1041 include:

[0076] Step S10411: Update the planned driving route information in real time to obtain real-time planned driving route information.

[0077] Step S10412: Based on the real-time planned driving route information, generate correction coefficients and estimated environmental complexity indicators.

[0078] For example, the server, based on the real-time traffic conditions corresponding to the planned driving route information (e.g., if traffic congestion occurs on the untraveled route), generates an estimated congestion duration. Then, based on the estimated congestion duration and the destination location information generated by the server, the server replans the untraveled route to obtain real-time planned driving route information. This allows the vehicle to travel according to the real-time planned driving route information, avoiding traffic congestion and enabling the vehicle to arrive at its destination earlier than the original planned route. Furthermore, the server quantifies the estimated driving environment complexity based on the real-time planned driving route information, thereby generating a correction coefficient and an estimated environment complexity index.

[0079] Step S105: For each vehicle, perform time complexity analysis processing based on each driving time to obtain the corresponding vehicle's time complexity index, where time complexity represents the probability of the vehicle causing an accident at different times.

[0080] For example, time complexity characterizes the probability of a vehicle causing an accident at different times. That is, the probability of a vehicle causing an accident varies at different times; the higher the probability of an accident, the larger the quantified value of the time complexity index. Therefore, by determining the vehicle's travel time, the probability of an accident at a corresponding time is obtained, thus yielding the corresponding vehicle's time complexity index. Furthermore, the travel time in this application embodiment can be specified down to the hour, minute, or second level, or it can be divided into time intervals, or it can be a natural day-level time interval determined based on peak travel periods during holidays. In other words, this application does not impose specific limitations on the time dimension and time span of the travel time.

[0081] Step S106: Determine the comprehensive complexity index for each vehicle based on the environmental complexity index and the time complexity index.

[0082] For example, the environmental complexity index is envi_0, and the time complexity index is time_0. The server performs a weighted calculation on the environmental complexity index envi_0 and the time complexity index time_0 for each vehicle, and then obtains the corresponding comprehensive complexity index comp_0. The calculation formula is shown in equation (3).

[0083] (3)

[0084] Where c and d are weighting coefficients.

[0085] Step S107: Obtain the carrying threshold of the comprehensive complexity index of the remote assisted driving personnel on the remote assistance terminal.

[0086] Step S108: Based on the load threshold and the comprehensive complexity index of each vehicle, determine the target number of vehicles that can be controlled by the remote assisted driving personnel and the target vehicle information among multiple vehicles to be controlled.

[0087] For example, since the driving age and experience of remote-assisted drivers differ across different remote-assisted terminals, and consequently, the overall complexity index carrying threshold varies for each remote-assisted driver. Therefore, the server determines the corresponding overall complexity index carrying threshold using the login information of the remote-assisted drivers on the remote-assisted terminals. Further, based on the carrying threshold and the overall complexity index of each vehicle, the server can determine the target number of vehicles that the remote-assisted driver can control, as well as the target vehicle information, among multiple vehicles to be controlled. Specifically, for example... Figure 5 A schematic diagram illustrating the determination of a target number of controllable vehicles is provided in an embodiment of this application, such as... Figure 5 As shown, the carrying threshold for remote assisted driving personnel pers_1 is 16, the carrying threshold for remote assisted driving personnel pers_2 is 10, the comprehensive complexity index of vehicle car_1 is 3, the comprehensive complexity index of vehicle car_2 is 6, the comprehensive complexity index of vehicle car_3 is 4, the comprehensive complexity index of vehicle car_4 is 5, and the comprehensive complexity index of vehicle car_5 is 7; furthermore, in one possible implementation, such as Figure 5 As shown in (a), based on the carrying threshold of remote assisted driving person pers_1 being 16, the controllable vehicles corresponding to remote assisted driving person pers_1 are determined to be vehicles car_1, car_4, and car_5. Based on the carrying threshold of remote assisted driving person pers_2 being 10, the controllable vehicles corresponding to remote assisted driving person pers_2 are determined to be vehicles car_2 and car_3. In another possible implementation, as follows... Figure 5 As shown in (b), based on the carrying threshold of remote assisted driving person pers_1 being 16, the controllable vehicles corresponding to remote assisted driving person pers_1 are determined to be vehicle car_3, vehicle car_4 and vehicle car_5. Based on the carrying threshold of remote assisted driving person pers_2 being 10, the controllable vehicles corresponding to remote assisted driving person pers_2 are determined to be vehicle car_1 and vehicle car_2.

[0088] Step S109: The target number of controllable vehicles and the target vehicle information are sent to the remote assistance terminal so that the remote assistance terminal displays real-time driving images of the target number of vehicles on the display unit. The real-time driving images of the vehicles are used to instruct the remote assisted driver to perform remote assisted driving on the corresponding controllable vehicles.

[0089] For example, after the server determines the target number of controllable vehicles and the target vehicle information, it can send the target number of controllable vehicles and the target vehicle information to the remote assistance terminal, so that the remote assistance terminal can display the real-time driving images of the target number of vehicles on the display unit, thereby enabling remote assistance drivers to remotely assist driving the corresponding controllable vehicles. Specifically, for example, the server determines that the controllable vehicles corresponding to the remote assisted driving person pers_2 are vehicles car_2 and car_3, based on the carrying threshold of 10. Then, the server sends the target vehicle information of vehicles car_2 and car_3 to the remote assistance terminal of the remote assisted driving person pers_2, so that the remote assistance terminal of the remote assisted driving person pers_2 displays real-time driving images of vehicles car_2 and car_3 on the display unit, thereby enabling the remote assisted driving person pers_2 to remotely assist driving vehicles car_2 and car_3. Taking vehicle car_2 as an example, when the remote assisted driving person pers_2 detects an obstacle in front of vehicle car_2 through the display unit, the remote assistance terminal responds to the trigger operation of the remote assisted driving person based on the obstacle in the vehicle driving image and generates a remote assistance command. Then, the server receives the remote assistance command sent by the remote assistance terminal and sends the remote assistance command to vehicle car_2, so that vehicle car_2 moves or stops according to the remote assistance command, thereby ensuring vehicle driving safety.

[0090] Furthermore, in one possible implementation, the specific implementation of the aforementioned server sending and receiving remote assistance commands includes: receiving remote assistance commands sent by the remote assistance terminal based on a public cloud RTM service; the remote assistance command is generated by the remote assistance terminal in response to the triggering operation of the remote assistance driver based on the vehicle driving image; and sending the remote assistance command to the corresponding target vehicle based on the public cloud RTM service, so that the target vehicle can drive or stop according to the remote assistance command.

[0091] In this embodiment, an image acquisition command is received from a remote auxiliary terminal; this command is then sent to the image acquisition units of multiple vehicles to be controlled, so that each vehicle's image acquisition unit acquires driving image information, the corresponding driving time, and the planned driving route information according to the command; the driving image information, driving time, and planned driving route information sent by each vehicle's image acquisition unit are received; for each vehicle, environmental complexity analysis is performed on the planned driving route information and driving image information to obtain the corresponding vehicle's environmental complexity index, where the environmental complexity index characterizes the complexity of the vehicle's driving environment; for each vehicle, time complexity analysis is performed based on each driving time to obtain the corresponding vehicle's time complexity index. The system employs a time complexity index for each vehicle, where time complexity represents the probability of an accident occurring at different times. Based on the environmental complexity index and the time complexity index, a comprehensive complexity index for each vehicle is determined. A carrying threshold for the comprehensive complexity index of the remote assisted driver is obtained. Based on the carrying threshold and the comprehensive complexity index of each vehicle, the target number of vehicles controllable by the remote assisted driver and their information are determined from among multiple vehicles to be controlled. The target number of controllable vehicles and their information are sent to the remote assisted driver, enabling the remote assisted driver to display real-time driving images of the target number of vehicles on the display unit. These real-time driving images are used to instruct the remote assisted driver to perform remote assisted driving on the corresponding controllable vehicles. By receiving and sending image acquisition commands to obtain driving image information, driving time, and planned driving route information sent by the image acquisition unit of each vehicle, the environmental complexity index of the corresponding vehicle is obtained based on the driving route information and driving image information, and the time complexity index of the corresponding vehicle is obtained based on the driving time, thus obtaining the comprehensive complexity index of each vehicle. Furthermore, combined with the carrying threshold of the comprehensive complexity index of the remote assisted driver at the remote assistance terminal, the target number of controllable vehicles and the target vehicle information are determined. Then, the server sends the target vehicle information to the remote assistance terminal based on the target number, so that the remote assistance terminal displays the real-time driving images of the target number of vehicles on the display unit, thereby enabling the remote assisted driver to remotely assist driving the corresponding (one or more) controllable vehicles, solving the problem of high operation and maintenance costs of unmanned vehicles caused by existing technical solutions.

[0092] Based on this, the method provided in this application embodiment further includes updating the target number of controllable vehicles based on the remote assisted driving duration of the remote assisted driver at the remote assistance terminal, so as to ensure vehicle driving safety. Specifically, this step includes: calculating the remote assisted driving duration of the remote assisted driver based on the driving time of the controllable vehicles to obtain the remote assisted driving duration; updating the target number based on the remote assisted driving duration to obtain the updated target number; determining the corresponding updated target vehicle information among multiple vehicles to be controlled based on the updated target number; and sending the updated target number and the updated target vehicle information to the remote assistance terminal so that the remote assistance terminal displays the real-time driving image of the updated target number of vehicles on the display unit.

[0093] Exemplary approach: To ensure vehicle driving safety, once the remote assisted driver begins remote assisted driving of the corresponding controllable vehicle, the remote assisted driving duration is calculated based on the vehicle's travel time. Further, the target number is updated based on the remote assisted driving duration, resulting in an updated target number. Since a longer remote assisted driving time leads to greater driver fatigue, the target number is reduced to ensure vehicle driving safety. The server then determines the updated target vehicle information from among multiple controllable vehicles based on the updated target number. The updated target number and target vehicle information are sent to the remote assisted driver, allowing the display unit to show real-time images of the updated target number of vehicles, thus enabling the remote assisted driver to remotely assist driving the corresponding controllable vehicle.

[0094] It is understood that the embodiments of this application do not specifically limit the vehicle type and purpose of the driverless vehicle. For example, the driverless vehicle can be an unmanned delivery vehicle, an unmanned road sweeper, an unmanned security patrol vehicle, or a driverless taxi or driverless bus.

[0095] Figure 6 This is a schematic diagram of the structure of a remote assisted driving control device provided in one embodiment of this application, as shown below. Figure 6 As shown, the remote assisted driving control device 3 provided in this embodiment includes:

[0096] Receiver module 31 is used to receive image acquisition commands sent by the remote auxiliary terminal;

[0097] The sending module 32 is used to send image acquisition instructions to the image acquisition units of multiple vehicles to be controlled, so that each vehicle's image acquisition unit can acquire driving image information, driving time corresponding to the driving image information, and planned driving route information according to the image acquisition instructions;

[0098] Receiver module 31 is used to receive driving image information, driving time and planned driving route information sent by the image acquisition unit of each vehicle.

[0099] The processing module 33 is used to perform environmental complexity analysis processing on the planned driving route information and driving image information for each vehicle to obtain the environmental complexity index of the corresponding vehicle, wherein the environmental complexity index represents the complexity of the vehicle's driving environment.

[0100] The processing module 33 is used to perform time complexity analysis processing on each vehicle according to each driving time to obtain the time complexity index of the corresponding vehicle, where the time complexity represents the probability of the vehicle having an accident at different times.

[0101] Processing module 33 is used to determine the comprehensive complexity index of each vehicle based on the environmental complexity index and the time complexity index.

[0102] The acquisition module 34 is used to acquire the carrying threshold of the comprehensive complexity index of the remote assisted driving personnel at the remote assistance terminal;

[0103] The processing module 33 is used to determine the target number of vehicles that can be controlled by the remote assisted driving personnel and the target vehicle information among multiple vehicles to be controlled, based on the load threshold and the comprehensive complexity index of each vehicle.

[0104] The sending module 32 is used to send the target number of controllable vehicles and target vehicle information to the remote assistance terminal, so that the remote assistance terminal can display real-time driving images of the target number of vehicles on the display unit. The real-time driving images of the vehicles are used to instruct the remote assisted driver to perform remote assisted driving on the corresponding controllable vehicles.

[0105] In one possible implementation, when processing module 33 performs environmental complexity analysis on the planned driving route information and driving image information to obtain the corresponding vehicle's environmental complexity index, it specifically performs the following: generating an environmental complexity correction coefficient and an estimated environmental complexity index based on the planned driving route information; performing environmental complexity analysis on the driving image information to obtain a real-time environmental complexity index; comparing the magnitude of the real-time environmental complexity index and the estimated environmental complexity index; if the real-time environmental complexity index is greater than or equal to the estimated environmental complexity index, then the real-time environmental complexity index is determined as the environmental complexity index; if the real-time environmental complexity index is less than the estimated environmental complexity index, then the environmental complexity index is obtained based on the correction coefficient and the real-time environmental complexity index.

[0106] In one possible implementation, when the processing module 33 generates the environmental complexity correction coefficient and the estimated environmental complexity index based on the planned driving route information, it is specifically used to: update the planned driving route information in real time to obtain real-time planned driving route information; and generate the correction coefficient and the estimated environmental complexity index based on the real-time planned driving route information.

[0107] In one possible implementation, after sending the target number of controllable vehicles and the target vehicle information to the remote assistance terminal, the remote assisted driving control device 3 is further configured to: calculate the remote assisted driving duration of the remote assisted driver based on the driving time of the controllable vehicles, and obtain the remote assisted driving duration; update the target number based on the remote assisted driving duration, and obtain the updated target number; determine the corresponding updated target vehicle information among multiple vehicles to be controlled based on the updated target number; and send the updated target number and the updated target vehicle information to the remote assistance terminal, so that the remote assistance terminal displays the real-time driving image of the updated target number of vehicles on the display unit.

[0108] In one possible implementation, when the sending module 32 sends the image acquisition command to the image acquisition units of the multiple vehicles to be controlled, it is specifically used to: send the image acquisition command to the image acquisition units of the multiple vehicles to be controlled via the MQTT communication protocol.

[0109] In one possible implementation, when receiving driving image information sent by the image acquisition unit of each vehicle, the receiving module 31 is specifically used to: receive the compressed encoded data stream sent by the image acquisition unit of each vehicle through the public cloud RTC service; wherein the compressed encoded data stream is obtained by the image acquisition unit compressing and encoding the driving image information; and decode the compressed encoded data stream to obtain the driving image information.

[0110] In one possible implementation, after sending the target number of controllable vehicles and target vehicle information to the remote assistance terminal, the remote assisted driving control device 3 is further configured to: receive remote assistance commands sent by the remote assistance terminal based on a public cloud RTM service; the remote assistance commands are generated by the remote assistance terminal in response to a trigger operation by the remote assisted driver based on a vehicle driving image; and send the remote assistance commands to the corresponding target vehicles based on the public cloud RTM service, so that the target vehicles can drive or stop according to the remote assistance commands.

[0111] The receiving module 31, sending module 32, processing module 33, and acquiring module 34 are connected sequentially. The remote assisted driving control device 3 provided in this embodiment can perform the following... Figures 2-5 The technical solutions of any of the method embodiments shown are similar in implementation principle and technical effect, and will not be described again here.

[0112] Figure 7 A schematic diagram of the structure of the electronic device provided in this application. Figure 7 As shown, the electronic device 50 provided in this embodiment includes at least one processor 501 and a memory 502. Optionally, the device 50 further includes a communication component 503. The processor 501, memory 502, and communication component 503 are connected via a bus 504.

[0113] In a specific implementation, at least one processor 501 executes computer execution instructions stored in memory 502, causing at least one processor 501 to perform the above-described method.

[0114] The specific implementation process of processor 501 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0115] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0116] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0117] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0118] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0119] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0120] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0121] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0122] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0123] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0124] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0125] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0126] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0127] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A remote assisted driving control method, characterized in that, Applied to a server, the method includes: Receive image acquisition commands sent by the remote auxiliary terminal; The image acquisition command is sent to the image acquisition units of multiple vehicles to be controlled, so that each vehicle's image acquisition unit can acquire driving image information, the driving time corresponding to the driving image information, and the planned driving route information according to the image acquisition command; Receive driving image information, driving time, and planned driving route information sent by the image acquisition unit of each vehicle; For each vehicle, the planned driving route information is updated in real time to obtain real-time planned driving route information. Based on the real-time planned driving route information, a correction coefficient and an estimated environmental complexity index are generated. The driving image information is processed to analyze environmental complexity, resulting in a real-time environmental complexity index. The real-time environmental complexity index and the estimated environmental complexity index are compared. If the real-time environmental complexity index is greater than or equal to the estimated environmental complexity index, then the real-time environmental complexity index is determined as the environmental complexity index. If the real-time environmental complexity index is less than the estimated environmental complexity index, then the environmental complexity index is obtained based on the correction coefficient and the real-time environmental complexity index, wherein the environmental complexity index characterizes the complexity of the vehicle's driving environment. For each vehicle, time complexity analysis is performed based on each driving time to obtain the corresponding vehicle's time complexity index, where the time complexity characterizes the probability of the vehicle experiencing an accident at different times. Based on the environmental complexity index and the time complexity index, a comprehensive complexity index is determined for each vehicle; wherein, the comprehensive complexity index is a value calculated in real time by the server based on the received driving image information, driving time and planned driving route information, used to quantitatively characterize the current driving environment complexity of the corresponding vehicle. Obtain the carrying threshold of the comprehensive complexity index of the remote assisted driving personnel at the remote assistance terminal; Based on the load threshold and the comprehensive complexity index of each vehicle, the target number of vehicles that the remote assisted driving personnel can control and the target vehicle information are determined among the multiple vehicles to be controlled. The target number of controllable vehicles and the target vehicle information are sent to the remote assistance terminal, so that the remote assistance terminal displays real-time driving images of the target number of vehicles on the display unit. The real-time driving images of the vehicles are used to instruct the remote assisted driver to perform remote assisted driving on the corresponding controllable vehicles.

2. The method according to claim 1, characterized in that, After sending the target number of controllable vehicles and the target vehicle information to the remote assistance terminal, the method further includes: Based on the driving time of the controllable vehicle, the remote assisted driving time of the remote assisted driver is calculated to obtain the remote assisted driving time. The target quantity is updated based on the duration of the remote assisted driving to obtain the updated target quantity; Based on the updated target number, determine the corresponding updated target vehicle information among the plurality of vehicles to be controlled; The updated target number and the updated target vehicle information are sent to the remote assistance terminal, so that the remote assistance terminal displays the real-time driving image of the updated target number of vehicles on the display unit.

3. The method according to claim 1, characterized in that, The step of sending the image acquisition command to the image acquisition units of multiple vehicles to be controlled includes: The image acquisition command is sent to the image acquisition units of multiple vehicles to be controlled via the MQTT communication protocol.

4. The method according to claim 1, characterized in that, The receipt of driving image information sent by the image acquisition unit of each vehicle includes: The public cloud RTC service receives compressed and encoded data streams sent by the image acquisition units of each vehicle; wherein the compressed and encoded data streams are obtained by the image acquisition units compressing and encoding the driving image information. The compressed encoded data stream is decoded to obtain the driving image information.

5. The method according to claim 1, characterized in that, After sending the target number of controllable vehicles and the target vehicle information to the remote assistance terminal, the method further includes: Based on the public cloud RTM service, the system receives remote assistance commands sent by the remote assistance terminal; the remote assistance commands are generated by the remote assistance terminal in response to the triggering operation of the remote assistance driver based on the real-time driving image of the vehicle. Based on the public cloud RTM service, the remote assistance command is sent to the corresponding target vehicle so that the target vehicle can drive or stop according to the remote assistance command.

6. A remote assisted driving control device, characterized in that, Applied to servers, including: The receiving module is used to receive image acquisition commands sent by the remote auxiliary terminal; The sending module is used to send the image acquisition command to the image acquisition units of multiple vehicles to be controlled, so that the image acquisition unit of each vehicle can acquire driving image information, driving time corresponding to the driving image information, and planned driving route information according to the image acquisition command; The receiving module is used to receive driving image information, driving time, and planned driving route information sent by the image acquisition unit of each vehicle. The processing module is used to update the planned driving route information in real time for each vehicle to obtain real-time planned driving route information; generate a correction coefficient and an estimated environmental complexity index based on the real-time planned driving route information; perform environmental complexity analysis processing on the driving image information to obtain a real-time environmental complexity index; compare the magnitude relationship between the real-time environmental complexity index and the estimated environmental complexity index; if the real-time environmental complexity index is greater than or equal to the estimated environmental complexity index, then the real-time environmental complexity index is determined as the environmental complexity index; if the real-time environmental complexity index is less than the estimated environmental complexity index, then the environmental complexity index is obtained based on the correction coefficient and the real-time environmental complexity index, wherein the environmental complexity index characterizes the complexity of the vehicle's driving environment. The processing module is used to perform time complexity analysis processing on each vehicle according to each driving time to obtain the time complexity index of the corresponding vehicle, wherein the time complexity represents the probability of the vehicle having an accident at different times. The processing module is used to determine the comprehensive complexity index of each vehicle based on the environmental complexity index and the time complexity index; wherein, the comprehensive complexity index is a value calculated in real time by the server based on the received driving image information, driving time and planned driving route information, which is used to quantitatively characterize the current driving environment complexity of the corresponding vehicle. The acquisition module is used to acquire the carrying threshold of the comprehensive complexity index of the remote assisted driving personnel of the remote assistance terminal; The processing module is used to determine the target number of vehicles that the remote assisted driving personnel can control and the target vehicle information among the multiple vehicles to be controlled, based on the load threshold and the comprehensive complexity index of each vehicle. The sending module is used to send the target number of controllable vehicles and target vehicle information to the remote assistance terminal, so that the remote assistance terminal displays real-time driving images of the target number of vehicles on the display unit. The real-time driving images of the vehicles are used to instruct the remote assisted driver to perform remote assisted driving on the corresponding controllable vehicles.

7. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 5.

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