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

Abstract

The embodiment of the invention provides a remote auxiliary driving control method and device, a storage medium and a program product. On the basis of obtaining driving image information, driving time and planned driving route information sent by an image acquisition unit of each vehicle, an environment complexity index of the corresponding vehicle is obtained according to the driving route information and the driving image information, and a time complexity index of the corresponding vehicle is obtained according to the driving time; the comprehensive complexity index of each vehicle is obtained; determining a target number of corresponding controllable vehicles and target vehicle information in combination with a bearing threshold value of a remote auxiliary driver of a remote auxiliary end; the target vehicle information is sent to the remote auxiliary end based on the target number, so that the remote auxiliary end displays the target number of vehicle real-time driving images on the display unit, a driver is remotely assisted in conducting remote auxiliary driving on the corresponding controllable vehicle, and the problem that the operation and maintenance cost of the unmanned vehicle is high is solved.

Description

Technical Field

[0001] The present 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 Art

[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 driving of driverless vehicles, driving safety must be ensured first.

[0003] In the prior art, in order to ensure the driving safety of unmanned vehicles, one-to-one on-site vehicle followers are usually configured for unmanned vehicles so that in an emergency, the unmanned vehicle can be manually taken over and controlled before an accident occurs, thereby avoiding an accident.

[0004] However, the solutions of the existing technology lead to the problem of high operating and maintenance costs of unmanned vehicles. Summary of the invention

[0005] The embodiments of the present application provide a remote assisted driving control method, device, storage medium and program product to solve the problem of high operating and maintenance costs of unmanned vehicles.

[0006] In a first aspect, an embodiment of the present application provides a remote assisted driving control method, which is applied to a server, comprising: receiving an image acquisition instruction sent by a remote assistance terminal; sending the image acquisition instruction to image acquisition units of multiple vehicles to be controlled respectively, so that the image acquisition unit of each vehicle acquires driving image information, the driving time corresponding to the driving image information, and the planned driving route information according to the image acquisition instruction; receiving the driving image information, driving time and planned driving route information sent by the image acquisition unit of each vehicle; for each vehicle, performing environmental complexity analysis on the planned driving route information and the driving image information to obtain an environmental complexity index for the corresponding vehicle, wherein the environmental complexity index represents the complexity of the driving environment of the vehicle; for each vehicle, performing time complexity analysis according to each driving time. Processing to obtain a time complexity index of the corresponding vehicle, wherein the time complexity represents the probability of an accident occurring at different times for the vehicle; determining a comprehensive complexity index of each vehicle according to the environment complexity index and the time complexity index; obtaining a load threshold of the comprehensive complexity index of the remote assisted driver of the remote assistance terminal; based on the load threshold and the comprehensive complexity index of each vehicle, determining the target number of controllable vehicles and target vehicle information of the remote assisted driver among the multiple vehicles to be controlled; sending 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 a 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.

[0007] In a possible implementation, the environmental complexity analysis processing of the planned driving route information and the driving image information to obtain the environmental complexity index of the corresponding vehicle 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 real-time environmental complexity index with the estimated environmental complexity index; if the real-time environmental complexity index is greater than or equal to the estimated environmental complexity index, 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, obtaining the environmental complexity index based on the correction coefficient and the real-time environmental complexity index.

[0008] In a 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; generating the correction coefficient and the estimated environmental complexity index based on the real-time planned driving route information.

[0009] In a 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 time of the remote assisted driver according to the driving time of the controllable vehicle to obtain the remote assisted driving time; updating the target number according to the remote assisted driving time to obtain an updated target number; determining the corresponding updated target vehicle information among the multiple vehicles to be controlled according to 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 a possible implementation, sending the image acquisition instruction to the image acquisition units of the multiple vehicles to be controlled respectively includes: sending the image acquisition instruction to the image acquisition units of the multiple vehicles to be controlled respectively through the MQTT communication protocol.

[0011] In a possible implementation, the receiving of 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 a possible implementation, after sending the target number of controllable vehicles and target vehicle information to the remote assistance terminal, the method further includes: based on a public cloud RTM service, receiving a remote assistance instruction sent by the remote assistance terminal; the remote assistance instruction is generated by the remote assistance terminal in response to a trigger operation of a remote assistance driver based on the vehicle driving image; based on the public cloud RTM service, sending the remote assistance instruction to the corresponding target vehicle, so that the target vehicle drives or stops according to the remote assistance instruction.

[0013] In a second aspect, an embodiment of the present application provides a remote assisted driving control device, which is applied to a server, including:

[0014] A receiving module, used for receiving an image acquisition instruction sent by a remote auxiliary terminal;

[0015] A sending module, used for sending the image acquisition instruction to the image acquisition units of multiple vehicles to be controlled respectively, so that the image acquisition unit of each vehicle acquires driving image information, the driving time corresponding to the driving image information, and the planned driving route information according to the image acquisition instruction;

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

[0017] A processing module, for performing environmental complexity analysis 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 represents the complexity of the driving environment of the vehicle;

[0018] The processing module is used to perform time complexity analysis processing for each vehicle according to each driving moment to obtain a time complexity index of the corresponding vehicle, wherein the time complexity represents the probability of an accident occurring at different moments of the vehicle;

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

[0020] An acquisition module, used to acquire a carrying threshold of a comprehensive complexity index of a remote assisted driving person of the remote assistance terminal;

[0021] The processing module is used to determine the target number of vehicles that can be controlled by the remote assisted driver and 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 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 assistance driver to perform remote assisted driving on the corresponding controllable vehicles.

[0023] In a possible implementation, when the processing module performs environmental complexity analysis processing on the planned driving route information and the driving image information to obtain the environmental complexity index of the corresponding vehicle, it is specifically used to: generate an environmental complexity correction coefficient and an estimated environmental complexity index based on the planned driving route information; perform environmental complexity analysis processing on the driving image information to obtain a real-time environmental complexity index; compare the real-time environmental complexity index with the estimated environmental complexity index; if the real-time environmental complexity index is greater than or equal to the estimated environmental complexity index, determine 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, obtain the environmental complexity index based on the correction coefficient and the real-time environmental complexity index.

[0024] In a 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 a possible implementation, after the target number of controllable vehicles and the target vehicle information are sent to the remote assistance terminal, the remote assisted driving control device is further used to: calculate the remote assisted driving time of the remote assisted driving personnel according to the driving time of the controllable vehicle to obtain the remote assisted driving time; update the target number according to the remote assisted driving time to obtain an updated target number; determine the corresponding updated target vehicle information among the multiple vehicles to be controlled according to 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 a possible implementation, when the sending module sends the image acquisition instruction to the image acquisition units of multiple vehicles to be controlled respectively, it is specifically used to: send the image acquisition instruction to the image acquisition units of multiple vehicles to be controlled respectively through the MQTT communication protocol.

[0027] In a possible implementation, when the receiving module receives the driving image information sent by the image acquisition unit of each vehicle, it 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.

[0028] In a 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 used to: based on the public cloud RTM service, receive the remote assistance instruction sent by the remote assistance terminal; the remote assistance instruction is generated by the remote assistance terminal in response to the trigger operation of the remote assisted driving personnel according to the vehicle driving image; based on the public cloud RTM service, send the remote assistance instruction to the corresponding target vehicle, so that the target vehicle drives or stops according to the remote assistance instruction.

[0029] In a third aspect, an embodiment of the present application provides an electronic device, including: a memory, a processor;

[0030] The memory stores computer-executable instructions;

[0031] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0032] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementations of the first aspect.

[0033] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementation methods of the first aspect.

[0034] The remote assisted driving control method, device, storage medium and program product provided in the embodiments of the present application receive image acquisition instructions sent by a remote assistance terminal; send the image acquisition instructions to the image acquisition units of multiple vehicles to be controlled respectively, so that the image acquisition unit of each vehicle acquires driving image information, the driving time corresponding to the driving image information, and the planned driving route information according to the image acquisition instructions; receive the driving image information, driving time and planned driving route information sent by the image acquisition unit of each vehicle; for each vehicle, perform environmental complexity analysis processing on the planned driving route information and the driving image information to obtain the environmental complexity index of the corresponding vehicle, wherein the environmental complexity index represents the complexity of the driving environment of the vehicle; for each vehicle, perform time complexity analysis processing according to each driving time. The method comprises the following steps: obtaining a time complexity index of the corresponding vehicle, wherein the time complexity represents the probability of an accident occurring to the vehicle at different times; determining a comprehensive complexity index of each vehicle according to the environment complexity index and the time complexity index; obtaining a load threshold of the comprehensive complexity index of the remote assisted driver of the remote assistance terminal; determining a target number of controllable vehicles and target vehicle information of the remote assisted driver among the multiple vehicles to be controlled based on the load threshold and the comprehensive complexity index of each vehicle; sending the target number of controllable vehicles and the 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 a 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. Based on receiving and sending image acquisition instructions 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 according to the driving route information and driving image information, and 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; further, combined with the carrying threshold of the comprehensive complexity index of the remote assisted driver of the remote assistance terminal, the target number of corresponding controllable vehicles and 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 perform remote assisted driving of the corresponding (one or more) controllable vehicles, thereby solving the problem of high operating and maintenance costs of unmanned vehicles caused by existing technical solutions. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0036] Figure 1 A schematic diagram of a remote assisted driving control method provided in this application;

[0037] Figure 2 A flowchart of a remote assisted driving control method provided by one embodiment of the present application;

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

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

[0040] Figure 5 A schematic diagram of determining a target number of controllable vehicles provided in an embodiment of the present application;

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

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

[0043] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0044] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0045] In the technical solution of this application, the user personal information involved and the collection, storage, use, processing, transmission, provision and disclosure of data are in compliance with the 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, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0047] First, the terms involved in this application are explained:

[0048] MQTT communication protocol: Message Queuing Telemetry Transport, message queue telemetry transport, it is a message protocol based on the publish / subscribe paradigm, it works on top of the TCP / IP protocol family and is designed for remote devices with low hardware performance and environments with poor network conditions;

[0049] Public cloud RTC service: Real-Time Communication, or RTC for short, is a public cloud-based real-time audio and video communication solution that 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 that provides low-latency, high-reliability messaging and real-time data transmission capabilities.

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

[0052] Figure 1 A schematic diagram of a remote assisted driving control method provided in this application, such as Figure 1As shown, the specific application scenario of the present application is a scenario of providing remote assisted driving services to unmanned vehicles based on autonomous driving technology. The execution subject of the method provided in the embodiment of the present application may be an electronic control unit, a terminal device or a server. The server is used as the execution subject for explanation. During the driving of the unmanned vehicle, the driving safety of the vehicle must be ensured first. Based on the method provided by the present application, the server obtains driving image information collected by 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 match the remote assisted driving personnel of the remote assistance end; and then realizes displaying the real-time driving images of the target number of vehicles on the display unit of the remote assistance end, so as to realize the remote assisted driving of the remote assistance end. The driver performs remote assisted driving on the corresponding target number of controllable vehicles; exemplarily, the server receives the driving image information 1 sent by the image acquisition unit of the vehicle car_1, and sends it to the remote assistance terminal, and the display unit of the remote assistance terminal displays the driving image information 1 (video source 1), and also displays the driving image information 2 (video source 2) of the vehicle car_2; then, when the remote assisted driver finds through the display unit that an obstacle appears in front of the vehicle car_1, the remote assistance terminal generates a remote assistance instruction in response to the triggering operation of the remote assisted driver according to the obstacle in the vehicle driving image; then the server receives the remote assistance instruction sent by the remote assistance terminal, and sends the remote assistance instruction to the target vehicle, so that the target vehicle drives or stops according to the remote assistance instruction, thereby ensuring the driving safety of the vehicle.

[0053] In the prior art, in order to ensure the driving safety of unmanned vehicles, a one-to-one on-site follower is usually assigned to the unmanned vehicle, so that in an emergency, before the unmanned vehicle has an accident, the unmanned vehicle can be manually taken over and controlled to avoid an accident. However, the prior art solution leads to the problem of high operating and maintenance costs of unmanned vehicles.

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

[0055] Figure 2 A flowchart of a remote assisted driving control method provided by an embodiment of the present application is shown in FIG. Figure 2As shown, the execution subject of the remote assisted driving control method provided in this embodiment may be an electronic control unit, a terminal device or a server. By way of example, this embodiment is described with a server as the execution subject of the method of this embodiment. The remote assisted driving control method provided in this embodiment includes the following steps:

[0056] Step S101, receiving an image acquisition instruction sent by a remote assistant terminal.

[0057] Step S102, sending image acquisition instructions to the image acquisition units of multiple vehicles to be controlled respectively, so that the image acquisition unit of each vehicle acquires driving image information, the driving time corresponding to the driving image information, and the planned driving route information according to the image acquisition instructions.

[0058] Exemplarily, the planned driving route information is used to indicate the driving route planned by the vehicle to be controlled according to the departure point and the destination; then, the server receives the image acquisition instruction sent by the remote auxiliary terminal, and then sends the image acquisition instruction to the image acquisition units of multiple vehicles to be controlled, so that the image acquisition unit of each vehicle collects driving image information, the driving time corresponding to the driving image information, and the planned driving route information according to the image acquisition instruction; wherein the driving time corresponds to the video frame in the driving image information. Furthermore, for each vehicle, the present application does not impose specific restrictions on the number of videos collected by its image acquisition unit. For example, if the vehicle has only one front road image acquisition unit, the corresponding number of videos collected is 1, and the front road driving image information is obtained; if the vehicle is provided with a front road image acquisition unit, a rear road image acquisition unit, a left road image acquisition unit, and a right road image acquisition unit, the corresponding number of videos collected is 4, and the front road driving image information, the rear road driving image information, the left road driving image information, and the right road driving image information are obtained. Among them, the vehicle to be controlled is Figure 1 The scenario shown introduces an unmanned vehicle.

[0059] In a possible implementation, the server sends the image acquisition instructions to the image acquisition units of multiple vehicles to be controlled respectively through the MQTT communication protocol; the method of sending the image acquisition instructions through the MQTT communication protocol ensures the high reliability of instruction transmission.

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

[0061] Exemplarily, 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 instruction, 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 encoding the driving image information, driving time and planned driving route information by the image acquisition unit; and then after receiving the encoded data, the encoded data is decoded to obtain the driving image information, driving time and planned driving route information sent by the image acquisition unit of each vehicle.

[0062] In a possible implementation, the server receives the compressed coded data stream sent by the image acquisition unit of each vehicle through the public cloud RTC service; the compressed coded data stream is obtained by the image acquisition unit compressing and encoding the driving image information; and then the server decodes the compressed coded data stream to obtain the driving image information. Furthermore, for the driving time and planned driving route information, the server can also receive the corresponding compressed coded data stream through the public cloud RTC service and then decode it.

[0063] Step S104, for each vehicle, the planned driving route information and the driving image information are analyzed for environmental complexity to obtain an environmental complexity index of the corresponding vehicle, wherein the environmental complexity index represents the complexity of the driving environment of the vehicle.

[0064] Exemplarily, 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, and then obtains the corresponding environmental complexity index. Specifically, taking any vehicle to be controlled as an example, for example, the vehicle to be controlled is an unmanned express delivery vehicle, the server determines the location information of the corresponding destination based on the express delivery task of the unmanned express delivery vehicle, and then generates the 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 according to 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, for example, according to the environmental characteristics such as vehicle flow in the driving image information, pedestrian flow around the vehicle, and the width of the road, and quantifies the second environmental complexity index envi_2; further, 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 formula (1).

[0065] (1)

[0066] Among them, 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, generating an environment complexity correction coefficient and an estimated environment complexity index according to the planned driving route information.

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

[0070] Step S1043, comparing the magnitude relationship between the real-time environment complexity index and the estimated environment complexity index.

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

[0072] Step S1045: If the real-time environment complexity index is less than the estimated environment complexity index, the environment complexity index is obtained according to the correction coefficient and the real-time environment complexity index.

[0073] Exemplarily, the server quantifies the estimated driving environment complexity according to the planned driving route information, generates an estimated environment complexity index EN_1, and a corresponding environment complexity correction coefficient x; further, the server performs real-time environment complexity analysis according to the driving image information, for example, based on environmental features such as vehicle flow in the driving image information, pedestrian flow around the vehicle, and road width, performs real-time environment complexity analysis to obtain a real-time environment complexity index EN_2; then, compares the size relationship between 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 index EN_2 obtained by the server based on the quantification of the planned driving route information is The real-time environment complexity index EN_2 is lower than the real-time environment complexity index determined based on the driving image information. Therefore, based on the basic principle of ensuring driving safety, the real-time environment complexity index EN_2 is determined as the environment complexity index EN_0. If the real-time environment complexity index EN_2 is less than the estimated environment complexity index EN_1, it means that the estimated environment complexity obtained by the server based on the planned driving route information is higher than the real-time environment complexity determined based on the 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 the remote assisted driver" determined in the subsequent steps from changing frequently, the real-time environment complexity index EN_2 is corrected according to the correction coefficient x to obtain the environment complexity index EN_0. The calculation formula for the correction calculation is shown in formula (2).

[0074] (2)

[0075] Furthermore, in yet another possible implementation, Figure 4 for Figure 3 The schematic diagram of the specific implementation steps of step S1041 in the embodiment shown is as follows: 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, generating a correction coefficient and an estimated environmental complexity index based on the real-time planned driving route information.

[0078] Exemplarily, based on the real-time road conditions corresponding to the planned driving route information, for example, if a driving route that has not been driven has experienced vehicle congestion, the server generates an estimated congestion duration based on the vehicle congestion situation, and then re-plans the driving route that has not been driven based on the estimated congestion duration and the location information of the destination generated by the server, and obtains the real-time planned driving route information, so that the vehicle can travel according to the real-time planned driving route information to avoid vehicle congestion, thereby achieving that the vehicle arrives at the destination earlier than the original planned driving route; and then the server quantifies the estimated complexity of the driving environment based on the real-time planned driving route information, and can generate a correction coefficient and an estimated environmental complexity index.

[0079] Step S105 , for each vehicle, a time complexity analysis is performed according to each driving moment to obtain a time complexity index of the corresponding vehicle, wherein the time complexity represents the probability of an accident occurring at different moments of the vehicle.

[0080] Exemplarily, the time complexity characterizes the probability of a vehicle having an accident at different times, that is, at different times, the probability of a vehicle having an accident is different, and the greater the probability of a vehicle having an accident, the greater the quantitative value corresponding to the time complexity index; and then through the driving time of the vehicle, the probability of the vehicle having an accident at the corresponding time is determined, and then the time complexity index of the corresponding vehicle is obtained. Furthermore, the driving time in the embodiment of the present application can be specific to the time point at the hour level, the time point at the minute level, and the time point at the second level, and can also be divided into time intervals, and can also be based on the natural day-level time interval determined based on the peak period of vehicle travel during holidays, that is, the present application does not make specific restrictions on the time dimension and time span of the driving time.

[0081] Step S106, determining the comprehensive complexity index of each vehicle according to the environment 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 weighted calculations on the environmental complexity index envi_0 and the time complexity index time_0 of each vehicle, and then obtains the corresponding comprehensive complexity index comp_0. The calculation formula is shown in formula (3):

[0083] (3)

[0084] Among them, c and d are weighting coefficients.

[0085] Step S107, obtaining a carrying threshold of a comprehensive complexity index of a remote assisted driving personnel at a 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 driver and the target vehicle information among multiple vehicles to be controlled.

[0087] For example, since the driving age and driving experience of remote assisted drivers at different remote assistance terminals are different, and thus the load thresholds of the comprehensive complexity index of different remote assisted drivers are different, the server determines the load threshold of the corresponding comprehensive complexity index through the login information of the remote assisted driver at the remote assistance terminal. Furthermore, based on the load threshold and the comprehensive complexity index of each vehicle, the server can determine the target number of vehicles that can be controlled by the remote assisted driver and the target vehicle information among multiple vehicles to be controlled. Specifically, for example, Figure 5 A schematic diagram of determining a target number of controllable vehicles provided in an embodiment of the present application, such as Figure 5 As shown, the load threshold of the remote assisted driver pers_1 is 16, the load threshold of the remote assisted driver pers_2 is 10, the comprehensive complexity index of the vehicle car_1 is 3, the comprehensive complexity index of the vehicle car_2 is 6, the comprehensive complexity index of the vehicle car_3 is 4, the comprehensive complexity index of the vehicle car_4 is 5, and the comprehensive complexity index of the vehicle car_5 is 7; further, in a possible implementation, as Figure 5 As shown in (a), according to the carrying threshold of the remote assisted driver pers_1 being 16, it is determined that the controllable vehicles corresponding to the remote assisted driver pers_1 are vehicles car_1, vehicle car_4 and vehicle car_5; according to the carrying threshold of the remote assisted driver pers_2 being 10, it is determined that the controllable vehicles corresponding to the remote assisted driver pers_2 are vehicles car_2 and vehicle car_3; in another possible implementation, as Figure 5 As shown in (b), according to the carrying threshold of the remote assisted driver pers_1 being 16, it is determined that the controllable vehicles corresponding to the remote assisted driver pers_1 are vehicle car_3, vehicle car_4 and vehicle car_5. According to the carrying threshold of the remote assisted driver pers_2 being 10, it is determined that the controllable vehicles corresponding to the remote assisted driver pers_2 are vehicle car_1 and vehicle car_2.

[0088] Step S109, sending 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 assistance 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, the target number of controllable vehicles and the target vehicle information can be sent 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 the remote assisted driver to perform remote assisted driving of the corresponding controllable vehicles. Specifically, for example, based on the load threshold of the remote assisted driver pers_2 being 10, the server determines that the controllable vehicles corresponding to the remote assisted driver pers_2 are vehicles car_2 and car_3; then the server sends the target vehicle information of vehicles car_2 and car_3 to the remote assisted terminal of the remote assisted driver pers_2, so that the remote assisted terminal of the remote assisted driver pers_2 displays the real-time driving images of vehicles car_2 and car_3 on the display unit, thereby enabling the remote assisted driver pers_2 to perform remote assisted driving on vehicles car_2 and car_3; taking vehicle car_2 as an example, when the remote assisted driver pers_2 finds an obstacle in front of vehicle car_2 through the display unit, the remote assisted terminal generates a remote assistance instruction in response to the triggering operation of the remote assisted driver according to the obstacle in the vehicle driving image; then the server receives the remote assistance instruction sent by the remote assistance terminal, and sends the remote assistance instruction to vehicle car_2, so that vehicle car_2 drives or stops according to the remote assistance instruction, thereby ensuring the driving safety of the vehicle.

[0090] Furthermore, in a possible implementation method, the specific implementation method of the above-mentioned server sending and receiving remote assistance instructions includes: based on the public cloud RTM service, receiving the remote assistance instructions sent by the remote assistance terminal; the remote assistance instructions are generated by the remote assistance terminal in response to the trigger operation of the remote assistance driver according to the vehicle driving image; based on the public cloud RTM service, the remote assistance instructions are sent to the corresponding target vehicle, so that the target vehicle drives or stops according to the remote assistance instructions.

[0091] In this embodiment, an image acquisition instruction sent by a remote assistant terminal is received; the image acquisition instruction is sent to the image acquisition units of multiple vehicles to be controlled respectively, so that the image acquisition unit of each vehicle acquires driving image information, the driving time corresponding to the driving image information, and the planned driving route information according to the image acquisition instruction; the driving image information, the driving time and the planned driving route information sent by the image acquisition unit of each vehicle are received; for each vehicle, the planned driving route information and the driving image information are analyzed for environmental complexity to obtain the environmental complexity index of the corresponding vehicle, wherein the environmental complexity index represents the complexity of the driving environment of the vehicle; for each vehicle, the time complexity analysis is performed according to each driving moment to obtain the corresponding vehicle. The method comprises the steps of: determining a time complexity index of a vehicle, wherein the time complexity represents the probability of an accident occurring to the vehicle at different times; determining a comprehensive complexity index of each vehicle according to the environment complexity index and the time complexity index; obtaining a load threshold of the comprehensive complexity index of a remote assisted driver at a remote assistance terminal; determining a target number of controllable vehicles and target vehicle information of the remote assisted driver among multiple vehicles to be controlled based on the load threshold and the comprehensive complexity index of each vehicle; sending the target number of controllable vehicles and the 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 a 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. Based on receiving and sending image acquisition instructions 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 according to the driving route information and driving image information, and 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; further, combined with the carrying threshold of the comprehensive complexity index of the remote assisted driver of the remote assistance terminal, the target number of corresponding controllable vehicles and 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 perform remote assisted driving of the corresponding (one or more) controllable vehicles, thereby solving the problem of high operating and maintenance costs of unmanned vehicles caused by existing technical solutions.

[0092] On this basis, the method provided in the embodiment of the present application also includes updating the target number of controllable vehicles based on the remote assisted driving time of the remote assisted driver of the remote assistance end to ensure the driving safety of the vehicle; specifically, this step includes: calculating the remote assisted driving time of the remote assisted driver according to the driving time of the controllable vehicle to obtain the remote assisted driving time; updating the target number according to the remote assisted driving time to obtain an updated target number; determining the corresponding updated target vehicle information among multiple vehicles to be controlled according to the updated target number; sending the updated target number and the updated target vehicle information to the remote assistance end, so that the remote assistance end displays the real-time driving image of the updated target number of vehicles on the display unit.

[0093] Exemplarily, in order to ensure the driving safety of the vehicle, after the remote assisted driver at the remote assistance end starts to perform remote assisted driving on the corresponding controllable vehicle, the remote assisted driving time of the remote assisted driver is calculated according to the driving time of the controllable vehicle, and then the remote assisted driving time is obtained; further, the target number is updated based on the remote assisted driving time to obtain an updated target number, that is, the longer the remote assisted driving time, the more energy the remote assisted driver consumes, and in order to ensure the driving safety of the vehicle, the target number is reduced to obtain an updated target number; further, the server determines the corresponding updated target vehicle information among multiple vehicles to be controlled according to the updated target number; the updated target number and the updated target vehicle information are sent to the remote assistance end, so that the remote assistance end displays the real-time driving image of the updated target number of vehicles on the display unit, thereby enabling the remote assisted driver to perform remote assisted driving on the corresponding controllable vehicle.

[0094] It is understandable that the embodiments of the present application do not impose specific restrictions on the vehicle type and vehicle purpose of the unmanned vehicle. For example, the unmanned vehicle can be an unmanned express delivery vehicle, an unmanned road sweeper, an unmanned security patrol vehicle, and the unmanned vehicle can also be an unmanned taxi or an unmanned bus.

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

[0096] A receiving module 31, used for receiving an image acquisition instruction sent by a remote assistant terminal;

[0097] The sending module 32 is used to send the image acquisition instruction to the image acquisition units of the plurality of vehicles to be controlled, so that the image acquisition unit of each vehicle acquires the driving image information, the driving time corresponding to the driving image information, and the planned driving route information according to the image acquisition instruction;

[0098] The receiving module 31 is used to receive the 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 on the planned driving route information and the driving image information for each vehicle, and obtain an environmental complexity index of the corresponding vehicle, wherein the environmental complexity index represents the complexity of the driving environment of the vehicle;

[0100] The processing module 33 is used to perform time complexity analysis processing for each vehicle according to each driving moment to obtain a time complexity index of the corresponding vehicle, wherein the time complexity represents the probability of an accident occurring at different moments of the vehicle;

[0101] The processing module 33 is used to determine the comprehensive complexity index of each vehicle according to the environmental complexity index and the time complexity index;

[0102] An acquisition module 34 is used to acquire a carrying threshold of a comprehensive complexity index of a remote assisted driving person at a remote assistance terminal;

[0103] The processing module 33 is used to determine the target number of vehicles that can be controlled by the remote auxiliary driver and 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 displays the 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 assistance driver to perform remote assisted driving on the corresponding controllable vehicles.

[0105] In a possible implementation, when the processing module 33 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 is specifically used to: generate an environmental complexity correction coefficient and an estimated environmental complexity index based on the planned driving route information; perform environmental complexity analysis on the driving image information to obtain a real-time environmental complexity index; compare 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, determine 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, obtain the environmental complexity index based on the correction coefficient and the real-time environmental complexity index.

[0106] In a possible implementation, when the processing module 33 generates an environmental complexity correction coefficient and an 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 a correction coefficient and an estimated environmental complexity index based on the real-time planned driving route information.

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

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

[0109] In a possible implementation, when receiving the 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 compressing and encoding the driving image information by the image acquisition unit; and decode the compressed encoded data stream to obtain the driving image information.

[0110] In a possible implementation, after sending the target number of controllable vehicles and target vehicle information to the remote assistance end, the remote assisted driving control device 3 is also used to: based on the public cloud RTM service, receive remote assistance instructions sent by the remote assistance end; the remote assistance instructions are generated by the remote assistance end in response to the trigger operation of the remote assisted driver based on the vehicle driving image; based on the public cloud RTM service, send the remote assistance instructions to the corresponding target vehicle, so that the target vehicle drives or stops according to the remote assistance instructions.

[0111] The receiving module 31, the sending module 32, the processing module 33 and the acquiring module 34 are connected in sequence. The remote assisted driving control device 3 provided in this embodiment can execute the following steps: Figure 2-Figure 5 The technical solutions of any of the method embodiments shown have similar implementation principles and technical effects, which will not be repeated here.

[0112] Figure 7 This is 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 also includes a communication component 503. The processor 501, the memory 502 and the communication component 503 are connected via a bus 504.

[0113] In a specific implementation process, at least one processor 501 executes the computer-executable instructions stored in the memory 502, so that at least one processor 501 executes the above method.

[0114] The specific implementation process of the processor 501 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment 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 (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the invention can be directly implemented as a hardware processor, or can be implemented by a combination of hardware and software modules in the processor.

[0116] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (NVM), such as at least one disk storage.

[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. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of this application is not limited to only one bus or one type of bus.

[0118] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0119] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0120] The above-mentioned 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 memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0121] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium 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 be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0122] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

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

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

[0125] If the function is implemented in the form of 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 the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0126] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0127] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present 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 comprises: Receive image acquisition instructions sent by the remote auxiliary terminal; The image acquisition instruction is sent to the image acquisition units of the plurality of vehicles to be controlled respectively, so that the image acquisition unit of each vehicle acquires driving image information, the driving time corresponding to the driving image information, and the planned driving route information according to the image acquisition instruction; Receive driving image information, driving time and planned driving route information sent by the image acquisition unit of each vehicle; For each vehicle, performing environmental complexity analysis on the planned driving route information and the driving image information to obtain an environmental complexity index of the corresponding vehicle, wherein the environmental complexity index represents the complexity of the driving environment of the vehicle; For each vehicle, a time complexity analysis process is performed according to each driving moment to obtain a time complexity index of the corresponding vehicle, wherein the time complexity represents the probability of an accident occurring at different moments of the vehicle; Determining a comprehensive complexity index for each vehicle according to the environmental complexity index and the time complexity index; Obtaining a carrying threshold of a comprehensive complexity index of a remote assisted driving person at the remote assistance terminal; Based on the load threshold and the comprehensive complexity index of each vehicle, determining the target number of vehicles that can be controlled by the remote assisted driver and target vehicle information among the multiple vehicles to be controlled; The target number of controllable vehicles and 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 a display unit, and the real-time driving images of the vehicles are used to instruct the remote assistance driver to perform remote assisted driving on the corresponding controllable vehicles.

2. The method according to claim 1, characterized in that: The performing environmental complexity analysis on the planned driving route information and the driving image information to obtain an environmental complexity index corresponding to the vehicle includes: Generating an environmental complexity correction coefficient and an estimated environmental complexity index according to 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 relationship between the real-time environment complexity index and the estimated environment complexity index; If the real-time environment complexity index is greater than or equal to the estimated environment complexity index, determining the real-time environment complexity index as the environment complexity index; If the real-time environment complexity index is less than the estimated environment complexity index, the environment complexity index is obtained according to the correction coefficient and the real-time environment complexity index.

3. The method according to claim 2, characterized in that The generating of the environmental complexity correction coefficient and the estimated environmental complexity index according to the planned driving route information includes: The planned driving route information is updated in real time to obtain real-time planned driving route information; The correction coefficient and the estimated environmental complexity index are generated according to the real-time planned driving route information.

4. The method according to claim 1, characterized in that: After sending the target number of controllable vehicles and target vehicle information to the remote assistance terminal, the method further includes: Calculating the remote assisted driving time of the remote assisted driving personnel according to the driving time of the controllable vehicle to obtain the remote assisted driving time; Updating the target quantity according to the remote assisted driving duration to obtain an updated target quantity; According to the updated target quantity, determining corresponding updated target vehicle information among the multiple vehicles to be controlled; The updated target quantity and updated target vehicle information are sent to the remote assistance terminal, so that the remote assistance terminal displays the real-time driving images of the updated target number of vehicles on a display unit.

5. The method according to claim 1, characterized in that: The step of sending the image acquisition instruction to the image acquisition units of the plurality of vehicles to be controlled respectively comprises: The image acquisition instruction is sent to the image acquisition units of multiple vehicles to be controlled respectively through the MQTT communication protocol.

6. The method according to claim 1, characterized in that The receiving of driving image information sent by the image acquisition unit of each vehicle includes: Receiving a compressed coded data stream sent by an image acquisition unit of each vehicle through a public cloud RTC service; wherein the compressed coded data stream is obtained by the image acquisition unit compressing and encoding the driving image information; The compressed coded data stream is decoded to obtain the driving image information.

7. The method according to claim 1, characterized in that After sending the target number of controllable vehicles and target vehicle information to the remote assistance terminal, the method further includes: Based on the public cloud RTM service, receiving the remote assistance instruction sent by the remote assistance terminal; the remote assistance instruction is generated by the remote assistance terminal in response to the triggering operation of the remote assistance driver according to the vehicle driving image; Based on the public cloud RTM service, the remote assistance instruction is sent to the corresponding target vehicle, so that the target vehicle drives or stops according to the remote assistance instruction.

8. A remote assisted driving control device, characterized in that: Applicable to servers, including: A receiving module, used for receiving an image acquisition instruction sent by a remote auxiliary terminal; A sending module, used for sending the image acquisition instruction to the image acquisition units of multiple vehicles to be controlled respectively, so that the image acquisition unit of each vehicle acquires driving image information, the driving time corresponding to the driving image information, and the planned driving route information according to the image acquisition instruction; The receiving module is used to receive the driving image information, driving time and planned driving route information sent by the image acquisition unit of each vehicle. A processing module, for performing environmental complexity analysis 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 represents the complexity of the driving environment of the vehicle; The processing module is used to perform time complexity analysis processing for each vehicle according to each driving moment to obtain a time complexity index of the corresponding vehicle, wherein the time complexity represents the probability of an accident occurring at different moments of the vehicle; The processing module is used to determine the comprehensive complexity index of each vehicle according to the environmental complexity index and the time complexity index; An acquisition module, used to acquire a carrying threshold of a comprehensive complexity index of a remote assisted driving person of the remote assistance terminal; The processing module is used to determine the target number of vehicles that can be controlled by the remote assisted driver and 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 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 assistance driver to perform remote assisted driving on the corresponding controllable vehicles.

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

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

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