Intelligent networked vehicle remote control and monitoring method

Abstract

The invention relates to the technical field of intelligent networked vehicles, in particular to an intelligent networked vehicle remote control and monitoring method. The method comprises the following steps: acquiring real-time running state information and roadside sensing data of a vehicle, and generating a remote takeover request; receiving a control instruction sent by the cloud control platform, and feeding back a control execution result; calculating instruction interaction delay and network transmission delay; the stability of the communication environment is evaluated by combining the two, and the vehicle driving mode is dynamically adjusted. Furthermore, time delay is calculated according to heartbeat signals and control instruction timestamps in the communication link, the stability index of the communication environment is evaluated, and the safe driving speed is adjusted according to the stability. And when the communication environment stability is lower than the threshold value or the instruction interaction delay exceeds the threshold value, an emergency stop mechanism is triggered. According to the method, the safety and the stability of the intelligent network connection vehicle under remote control can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical fields of intelligent connected vehicles and remote control technology, and specifically relates to a method for remote control and monitoring of intelligent connected vehicles. Background Art

[0002] Intelligent connected vehicles play an increasingly important role in modern transportation systems, especially in autonomous driving, traffic management, and safety. Through vehicle-road collaborative control system technology, intelligent connected vehicles can achieve efficient communication between vehicles and road infrastructure, thereby improving traffic efficiency and safety. Currently, the remote control and monitoring technologies of intelligent connected vehicles have been widely studied and applied, but there are still some deficiencies in the existing technologies.

[0003] Existing remote control systems for intelligent connected vehicles usually rely on a single communication technology and sensing devices, such as on-vehicle cameras and radars. In complex traffic environments and adverse weather conditions, it is difficult to ensure the accuracy and real-time nature of data, thus affecting the control accuracy and safety of the vehicle. In addition, the existing systems lack comprehensive real-time monitoring and multi-device collaboration capabilities and cannot effectively respond to emergencies, such as traffic accidents or sudden intrusion of pedestrians. Summary of the Invention

[0004] The purpose of the present invention is to solve the deficiencies existing in the prior art, and a method for remote control and monitoring of intelligent connected vehicles is proposed.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions: A method for remote control and monitoring of intelligent connected vehicles, including:

[0006] Obtain the real-time operating status information of the vehicle and roadside perception data, and generate a remote takeover request based on the real-time operating status information and roadside perception data;

[0007] Receive the control instruction sent by the cloud control platform based on the remote takeover request, and feedback the control execution result to the cloud control platform;

[0008] Calculate the time difference between the control instruction and the control execution result as the instruction interaction delay;

[0009] Obtain the communication link quality parameters between the vehicle and the cloud control platform, and calculate the network transmission delay according to the communication link quality parameters; and

[0010] Evaluate the stability of the current communication environment by combining the instruction interaction delay and the network transmission delay, and dynamically adjust the driving mode of the vehicle according to the stability.

[0011] Further, calculate the round-trip delay of the link according to the sending time and response time of the heartbeat signal in the communication link;

[0012] Calculate the one-way transmission delay according to the timestamp of the control instruction sent by the cloud control platform;

[0013] Determine the network transmission delay by weighted summation of the round-trip delay of the link and the one-way transmission delay. The formula is as follows:

[0014]

[0015] Among them, Tnet represents the network transmission delay, Trtt represents the round-trip delay of the link, Towd represents the one-way transmission delay, and α is the weight coefficient, and the value range is .

[0016] Further, obtain the received timestamp of the control instruction sent by the cloud control platform;

[0017] Calculate the instruction interaction delay according to the received timestamp and the local processing completion timestamp; and

[0018] Combined with the network transmission delay and the instruction interaction delay, use the following formula to evaluate the stability index of the communication environment:

[0019]

[0020] Among them, T represents the stability index of the communication environment, Tnet represents the network transmission delay, Tcmd represents the instruction interaction delay, and e is the base of the natural logarithm.

[0021] Further, the steps of dynamically adjusting the vehicle driving mode according to the stability index of the communication environment include:

[0022] Obtain the target path planning information generated by the cloud control platform based on the roadside perception data;

[0023] Calculate the path deviation distance according to the current position of the vehicle and the target path planning information;

[0024] Combined with the network transmission delay and the path deviation distance, use the following formula to calculate the safe driving speed:

[0025]

[0026] Among them, Vsafe represents the safe driving speed, Vmax represents the maximum allowable speed of the vehicle, Tnet represents the network transmission delay, Ddev represents the path deviation distance, and Dmax represents the maximum allowable deviation distance; and

[0027] Adjust the driving mode of the vehicle according to the safe driving speed.

[0028] Further, when the stability index of the communication environment is lower than a preset threshold, an emergency stop mechanism is triggered, which specifically includes:

[0029] If the network transmission delay exceeds the delay upper limit threshold, it is determined that the communication environment is unstable; and

[0030] Send an emergency stop instruction to the vehicle controller to control the vehicle to decelerate and stop.

[0031] Further, when the instruction interaction delay exceeds the disconnection detection threshold, a disconnection protection mechanism is triggered, which specifically includes:

[0032] Determine that the communication link is interrupted; and

[0033] Send an emergency stop instruction to the vehicle controller to control the vehicle to enter a safe parking state.

[0034] An intelligent connected vehicle remote control and monitoring method includes:

[0035] Receive the real-time operation status information and roadside perception data uploaded by the vehicle, and generate a control instruction based on the real-time operation status information and roadside perception data;

[0036] Obtain the frame rate and transmission time parameters of the roadside perception data, and calculate the perception data transmission delay according to the frame rate and transmission time parameters;

[0037] If the perception data transmission delay exceeds the preset threshold, send an emergency stop instruction to the vehicle; and

[0038] Continuously monitor the perception data transmission delay. If the perception data transmission delay returns to the normal range, continue to send a control instruction to the vehicle.

[0039] Further, calculate the perception data transmission delay according to the perception data frame rate;

[0040] Calculate the perception data interaction delay according to the sending time and receiving time of the perception data frame; and

[0041] Combine the perception data transmission delay and the perception data interaction delay, and use the following formula to calculate the comprehensive perception delay:

[0042]

[0043] where Tsensor represents the comprehensive perception delay, Ttrans represents the perception data transmission delay, Tinteract represents the perception data interaction delay, and β is a weight coefficient, and the value range is .

[0044] Further, if the perception data transmission delay or the perception data interaction delay exceeds the preset threshold, generate an emergency stop instruction with the highest priority; and

[0045] Send an emergency stop command to the vehicle to control the vehicle to stop immediately.

[0046] Furthermore, continuously monitor the transmission delay and interaction delay of the perception data frame;

[0047] If both the perception data transmission delay and the interaction delay are continuously less than the threshold within the preset range, it is determined that the perception data transmission is normal; and

[0048] Generate a new control command based on the real-time operating status information and roadside perception data and send it to the vehicle.

[0049] An intelligent connected vehicle remote control and its monitoring device, comprising:

[0050] A status acquisition module, configured to acquire the real-time operating status information and roadside perception data of the vehicle and generate a remote takeover request;

[0051] An instruction interaction module, configured to receive the control command issued by the cloud control platform and feedback the control execution result to the cloud control platform;

[0052] A network monitoring module, configured to calculate the instruction interaction delay between the control command and the control execution result, obtain the communication link quality parameter and calculate the network transmission delay; and

[0053] A mode adjustment module, configured to evaluate the stability of the communication environment by combining the instruction interaction delay and the network transmission delay, and dynamically adjust the driving mode of the vehicle according to the stability.

[0054] Furthermore, the network monitoring module is further configured to calculate the link round-trip delay according to the sending time and response time of the heartbeat signal; calculate the one-way transmission delay according to the timestamp of the control command; and determine the network transmission delay according to the link round-trip delay and the one-way transmission delay.

[0055] Furthermore, the network monitoring module is further configured to obtain the reception timestamp of the control command; calculate the instruction interaction delay according to the reception timestamp and the local processing completion timestamp; and evaluate the stability of the communication environment by combining the network transmission delay and the instruction interaction delay.

[0056] Furthermore, the mode adjustment module is further configured to obtain the target path planning information generated by the cloud control platform; calculate the path deviation distance according to the current position of the vehicle and the target path planning information; calculate the safe driving speed by combining the network transmission delay and the path deviation distance; and adjust the driving mode of the vehicle according to the safe driving speed.

[0057] Furthermore, the mode adjustment module is further configured to trigger an emergency stop mechanism when the stability index of the communication environment is lower than the preset threshold; and send an emergency stop command to the vehicle controller to control the vehicle to decelerate and stop.

[0058] Further, the mode adjustment module is further configured to trigger a disconnection protection mechanism when the instruction interaction delay exceeds the disconnection detection threshold; and send an emergency stop instruction to the vehicle controller to control the vehicle to enter a safe parking state.

[0059] An intelligent networked vehicle remote control and monitoring device includes:

[0060] A data acquisition module, configured to receive real-time operation status information and roadside perception data uploaded by the vehicle, and generate a control instruction;

[0061] A perception monitoring module, configured to obtain the frame rate and transmission time parameters of the roadside perception data, and calculate the perception data transmission delay;

[0062] An instruction issuing module, configured to send an emergency stop instruction to the vehicle when the perception data transmission delay exceeds a preset threshold; and the perception monitoring module is further configured to continuously monitor the perception data transmission delay, and if the perception data transmission delay returns to the normal range, continue to send a control instruction to the vehicle.

[0063] A computer device includes a memory and one or more processors. When computer-readable instructions stored in the memory are executed by the processors, the one or more processors are caused to implement the steps in the above method embodiments.

[0064] One or more non-transitory computer-readable storage media storing computer-readable instructions, which when executed by one or more processors, cause the one or more processors to implement the steps in the above method embodiments.

[0065] Details of one or more embodiments of the present application are set forth in the following drawings and description. Other features and advantages of the present application will become apparent from the specification, the drawings, and the claims.

[0066] In summary, the beneficial effects of the present invention are:

[0067] The present invention proposes a method for remote control and monitoring of intelligent connected vehicles based on the ROS (Robot Operating System) framework. This method manages intelligent transportation facilities through a road test control platform, such as intelligent sensors (millimeter-wave radar, lidar), monitoring devices, parameter settings, and displays the real-time working status, realizing the collaborative control of vehicles and roadside devices. Specifically, the vehicle end of the invention controls the core system of the vehicle through the ROS framework, and the road test service system integrates multiple high-performance devices, including 32-line lidar, 32-line blind spot filling lidar, roadside intelligent cameras, traffic lights, edge computing platforms, RSUs, traffic sign displays, GNSS base station receivers, wireless access points (APs), and roadside switches, etc.

[0068] These devices are integrated on a remotely controllable movable bracket, enabling real-time, high-resolution environmental perception and data transmission. The 32-line lidar has a wide field of view and high resolution, and can obtain real-time distance and reflectivity data within a long distance and a 360-degree horizontal field of view. The 32-line blind spot filling lidar solves the problem of inaccurate detection below, ensuring all-round environmental perception. The roadside intelligent camera supports high-definition encoding and behavior analysis, and can sense pedestrians and motor vehicles on the road in real time. The edge computing platform and the roadside switch provide powerful data processing and transmission capabilities, ensuring the efficient operation of the system.

[0069] In addition, the present invention, through the intelligent cloud basic control platform, monitors the operating parameters and status of each vehicle in the scene in real time, as well as the operating conditions of roadside devices, and discovers and processes various emergencies in a timely manner. The central server is deployed in the middle to issue events, realizing the remote control and monitoring of driverless vehicles. Through this series of technological innovations, the present invention effectively improves the control accuracy, safety, and reliability of intelligent connected vehicles, and has important practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 It is a schematic flowchart of a method for remote control and monitoring of intelligent connected vehicles provided by an embodiment of the present invention.

[0071] Figure 2 It is a logic block diagram of the calculation process of network transmission delay and command interaction delay in an embodiment of the present invention.

[0072] Figure 3 It is a schematic structural diagram of the perception data transmission delay and interaction delay monitoring and processing mechanism in an embodiment of the present invention.

[0073] Figure 4 It is a functional module structure diagram of a device for remote control and monitoring of intelligent connected vehicles provided by an embodiment of the present invention.

[0074] Reference numerals: 101, status acquisition module; 102, instruction interaction module; 103, network monitoring module; 104, mode adjustment module; 105, perception monitoring module. Detailed implementation manners

[0075] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of this application.

[0076] The present invention provides a method, device and computer device for remote control and monitoring of an intelligent connected vehicle. The core lies in dynamically evaluating the stability of the communication environment by obtaining vehicle operation state information and roadside perception data in real time, combining with the instruction interaction mechanism of the cloud control platform, and adjusting the vehicle driving mode or triggering an emergency protection mechanism according to the evaluation result. The following will be combined with the attached Figure 1 to the attached Figure 4 to elaborate in detail on the specific implementation manners of the present invention.

[0077] In practical applications, as Figure 1 shown, the method of the present invention first has the status acquisition module 101 acquire the real-time operation state information of the vehicle and roadside perception data. These data include but are not limited to the vehicle speed, acceleration, steering wheel angle, accelerator pedal position, brake state, and traffic signal state, road obstacle information, etc. from the roadside unit. The status acquisition module 101 integrates the above data to generate a remote takeover request and sends it to the cloud control platform. The cloud control platform generates a control instruction based on the received remote takeover request and sends the instruction to the vehicle. The instruction interaction module 102 on the vehicle side receives the control instruction sent by the cloud control platform and feeds back the control execution result to the cloud control platform. This process constitutes the core interaction process of the entire remote control.

[0078] To ensure the reliability of the remote control, the network monitoring module 103 is responsible for calculating the time difference between the control instruction and the control execution result, that is, the instruction interaction delay. Specifically, the network monitoring module 103 will record the timestamp of the control instruction sent by the cloud control platform and record the completion timestamp after local processing. The time difference between the two is the instruction interaction delay. In addition, the network monitoring module 103 also calculates the link round-trip delay through the sending time and response time of the heartbeat signal, and calculates the one-way transmission delay through the timestamp of the control instruction. The link round-trip delay and the one-way transmission delay determine the network transmission delay through weighted summation, and the formula is as follows:

[0079]

[0080] Among them, Tnet represents the network transmission delay, Trtt represents the link round-trip delay, Towd represents the one-way transmission delay, α is the weight coefficient, and its value range is , and the selection of the weight coefficient α depends on the requirements of specific application scenarios. For example, in scenarios with high requirements for real-time performance, the weight of the one-way transmission delay can be appropriately increased to more accurately reflect the network performance.

[0081] Combining the network transmission delay and the instruction interaction delay, the network monitoring module 103 further evaluates the stability index of the communication environment. The calculation formula of the stability index is as follows:

[0082]

[0083] Among them, T represents the stability index of the communication environment, Tnet represents the network transmission delay, Tcmd represents the instruction interaction delay, and e is the base of the natural logarithm. The value range of the stability index S is , the closer the value is to 1, the more stable the communication environment is, and vice versa, it means the communication environment is unstable. When the stability index is lower than the preset threshold, the system determines that the current communication environment is unreliable and triggers the corresponding protection mechanism.

[0084] The mode adjustment module 104 dynamically adjusts the driving mode of the vehicle according to the stability index of the communication environment. Specifically, the mode adjustment module 104 first obtains the target path planning information generated based on the roadside perception data from the cloud control platform, and calculates the path deviation distance in combination with the current position of the vehicle. The path deviation distance is used to measure the deviation degree between the current driving trajectory of the vehicle and the target path. Combining the network transmission delay and the path deviation distance, the mode adjustment module 104 uses the following formula to calculate the safe driving speed:

[0085]

[0086] Among them, Vsafe represents the safe driving speed, Vmax represents the maximum allowable speed of the vehicle, Tnet represents the network transmission delay, Ddev represents the path deviation distance, and Dmax represents the maximum allowable deviation distance. The design of this formula aims to compensate for the potential risks brought by the network transmission delay and the path deviation by reducing the vehicle speed, so as to ensure the safety of the vehicle in a complex communication environment.

[0087] When the stability index of the communication environment is lower than the preset threshold, the mode adjustment module triggers an emergency stop mechanism. Specifically, if the network transmission delay exceeds the upper limit threshold of the delay, the system determines that the communication environment is unstable and sends an emergency stop command to the vehicle controller to control the vehicle to decelerate and finally stop. In addition, if the instruction interaction delay exceeds the disconnection detection threshold, the system determines that the communication link is interrupted and triggers a disconnection protection mechanism. At this time, the mode adjustment module also sends an emergency stop command to the vehicle controller to control the vehicle to enter a safe parking state. The design of the emergency stop mechanism fully considers the potential safety hazards that may be brought by abnormal communication environments, ensuring that the vehicle can quickly take protective measures in extreme situations.

[0088] In terms of the monitoring of perception data, the present invention also provides a monitoring and processing mechanism for the transmission delay and interaction delay of roadside perception data, as Figure 3 shown. The perception monitoring module 105 is responsible for obtaining the frame rate and transmission time parameters of the roadside perception data, and calculating the perception data transmission delay accordingly. The calculation of the perception data transmission delay is based on the sending time and receiving time of the perception data frame, and the formula is as follows:

[0089]

[0090] Among them, Ttrans represents the perception data transmission delay, Trecv represents the receiving time of the perception data frame, and Tsend represents the sending time of the perception data frame. In addition, the perception monitoring module 105 also calculates the perception data interaction delay through the sending time and receiving time of the perception data frame, and the formula is as follows:

[0091]

[0092] Among them, Tinteract represents the perception data interaction delay, and Tprocess represents the local processing completion time. Combining the perception data transmission delay and the perception data interaction delay, the perception monitoring module 105 calculates the comprehensive perception delay using the following formula:

[0093]

[0094] Among them, Tsensor represents the comprehensive perception delay, Ttrans represents the perception data transmission delay, Tinteract represents the perception data interaction delay, and β is a weight coefficient, and the value range is . The calculation result of the comprehensive perception delay is used to evaluate the overall performance of the perception data transmission.

[0095] If the latency of perception data transmission or the delay of perception data interaction exceeds a preset threshold, the perception monitoring module 105 generates an emergency stop instruction with the highest priority and sends it to the vehicle to control the vehicle to stop immediately. The design of this mechanism aims to address the potential safety hazards caused by abnormal perception data transmission. At the same time, the perception monitoring module 105 continuously monitors the transmission latency and interaction delay of perception data frames. If both the perception data transmission latency and the interaction delay are continuously less than the threshold within the preset range, the system determines that the perception data transmission is normal and generates a new control instruction based on the real-time operation status information and roadside perception data and sends it to the vehicle.

[0096] In terms of hardware implementation, the present invention provides an intelligent connected vehicle remote control and monitoring device, and its functional module structure is as Figure 4 shown. The device includes a status acquisition module 101, an instruction interaction module 102, a network monitoring module 103, a mode adjustment module, and a perception monitoring module 105. The functions of each module have been described in detail in the foregoing content. In addition, the present invention also provides a computer device, including a memory and one or more processors. The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, one or more processors are caused to execute the steps in the method embodiment described above. The computer device can be an in-vehicle terminal, a cloud server, or other devices with computing capabilities.

[0097] In summary, the present invention dynamically evaluates the stability of the communication environment by obtaining the vehicle operation status information and roadside perception data in real time, combining with the instruction interaction mechanism of the cloud control platform, and adjusts the vehicle driving mode or triggers an emergency protection mechanism according to the evaluation result. Its design fully considers the safety and reliability requirements of intelligent connected vehicles in complex communication environments and has broad application prospects.

Claims

1. An intelligent connected vehicle remote control and monitoring method, comprising: Obtaining real-time operating status information of the vehicle and roadside perception data; Generating a remote takeover request according to the real-time operating status information and roadside perception data; Receiving a control instruction sent by the cloud control platform based on the remote takeover request; Feeding back the control execution result to the cloud control platform; Obtaining the time difference between the control instruction and the control execution result as the instruction interaction delay; Obtaining the communication link quality parameter between the vehicle and the cloud control platform, and calculating the network transmission delay according to the communication link quality parameter; Evaluating the stability of the current communication environment by combining the instruction interaction delay and the network transmission delay; Dynamically adjusting the driving mode of the vehicle according to the stability.

2. The method according to claim 1, wherein The method further comprises: Calculating the link round-trip delay according to the sending time and response time of the heartbeat signal in the communication link; Calculating the one-way transmission delay according to the timestamp of the control instruction issued by the cloud control platform; Determining the network transmission delay by weighted summation of the link round-trip delay and the one-way transmission delay.

3. The method according to claim 1, wherein The method further comprises: Obtaining the receiving timestamp of the control instruction issued by the cloud control platform; Calculating the instruction interaction delay according to the receiving timestamp and the local processing completion timestamp; Combining the network transmission delay and the instruction interaction delay.

4. The method according to claim 1, characterized in that, The dynamically adjusting the driving mode of the vehicle includes: Obtaining the target path planning information generated by the cloud control platform based on the roadside perception data; Calculating the path deviation distance according to the current position of the vehicle and the target path planning information; Combining the network transmission delay and the path deviation distance.

5. The method according to claim 1, wherein The method further comprises: When the stability index of the communication environment is lower than a preset threshold, triggering an emergency stop mechanism, specifically including: If the network transmission delay exceeds the delay upper limit threshold, it is determined that the communication environment is unstable; Sending an emergency stop instruction to the vehicle controller to control the vehicle to decelerate and stop.

6. The method according to claim 1, wherein The method further comprises: When the instruction interaction delay exceeds the disconnection detection threshold, triggering a disconnection protection mechanism, specifically including: Determining that the communication link is interrupted; Sending an emergency stop instruction to the vehicle controller to control the vehicle to enter a safe parking state.

7. An intelligent connected vehicle remote control and monitoring device, comprising: A status acquisition module (101) for obtaining real-time operating status information of the vehicle and roadside perception data and generating a remote takeover request; An instruction interaction module (102) for receiving a control instruction issued by the cloud control platform and feeding back the control execution result to the cloud control platform; A network monitoring module (103) for calculating the instruction interaction delay between the control instruction and the control execution result, obtaining the communication link quality parameter and calculating the network transmission delay; A mode adjustment module (104) for evaluating the stability of the communication environment by combining the instruction interaction delay and the network transmission delay and dynamically adjusting the driving mode of the vehicle according to the stability; A perception monitoring module (105) for obtaining the frame rate and transmission time parameter of the roadside perception data and calculating the perception data transmission delay.

8. The device according to claim 7, characterized in that The network monitoring module (103) is further used for: Calculating the link round-trip delay according to the sending time and response time of the heartbeat signal; Calculating the one-way transmission delay according to the timestamp of the control instruction; Determine the network transmission delay based on the link round-trip delay and the one-way transmission delay.

9. The device according to claim 7, characterized in that, The network monitoring module (103) is further configured to: Obtain the reception timestamp of the control instruction; Calculate the instruction interaction delay based on the reception timestamp and the local processing completion timestamp; Evaluate the stability of the communication environment by combining the network transmission delay and the instruction interaction delay.

10. The device according to claim 7, characterized in that, The mode adjustment module (104) is further configured to: Obtain the target path planning information generated by the cloud control platform; Calculate the path deviation distance based on the current vehicle position and the target path planning information; Calculate the safe driving speed by combining the network transmission delay and the path deviation distance; Adjust the driving mode of the vehicle according to the safe driving speed.

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