Unmanned vehicle speed limit control method in case of communication quality decline

Abstract

The invention relates to a speed limit control method for an unmanned vehicle when the communication quality is reduced, belongs to the technical field of unmanned vehicle control, and solves the problems that the speed of the unmanned vehicle cannot be adjusted in real time according to the communication quality, the driving safety is poor and the control stability of the unmanned vehicle is insufficient in the prior art. The method comprises the steps of collecting unmanned vehicle state data in real time, and calculating a communication quality score of an unmanned vehicle; comparing the communication quality score with the minimum standard score, and if the communication quality score is greater than or equal to the minimum standard score, continuing to collect the unmanned vehicle state data; if the score is smaller than the minimum standard score, controlling the unmanned vehicle to enter a weak communication mode; when the unmanned vehicle is in the weak communication mode, the communication signal strength of the unmanned vehicle is collected in real time, and the communication signal strength of the unmanned vehicle is compared with a first communication state threshold value; when the communication signal strength of the unmanned vehicle is smaller than a first communication state threshold value, road condition data of the unmanned vehicle are collected in real time; and performing speed limit control on the unmanned vehicle based on the communication signal strength of the unmanned vehicle and the road condition data.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned vehicle control, and in particular to a speed limit control method for an unmanned vehicle when communication quality decreases. Background Art

[0002] With the integration and development of communication technology and autonomous driving technology, unmanned vehicles are increasingly used in the field of modern transportation. The normal operation of unmanned vehicles is highly dependent on a stable and efficient communication system to achieve information exchange between vehicles and control centers, and between vehicles and surrounding environment equipment.

[0003] At present, although some unmanned vehicles have certain signal receiving and sending capabilities in terms of communication, the means of monitoring and evaluating communication quality are relatively simple, and they can often only simply determine whether the communication is interrupted, but cannot accurately quantify the quality of communication. In terms of speed control, most unmanned vehicles set the speed based on preset map data, road rules and the vehicle's own performance parameters, and lack a mechanism to dynamically adjust the speed according to real-time communication conditions.

[0004] Existing unmanned vehicle technology has the following shortcomings: First, it is impossible to accurately grasp the changes in communication quality in real time. When the communication quality deteriorates, it cannot be detected and responded to in time, which may cause control command delays or loss, affecting the driving safety and stability of the unmanned vehicle; second, it is not flexible enough in speed control and cannot adjust the vehicle speed according to the real-time status of communication quality. For example, when the communication quality is poor, if the vehicle still drives at the original speed, once an emergency occurs, due to communication delays, the vehicle cannot receive and execute the instructions of the control center in time, which can easily cause traffic accidents, and may cause safety hazards due to the inability to timely feedback vehicle status information. Summary of the invention

[0005] In view of the above analysis, an embodiment of the present invention aims to provide a method for controlling the speed limit of an unmanned vehicle when the communication quality decreases, so as to solve the problems that the existing unmanned vehicle cannot adjust the speed according to the communication quality in real time, the driving safety is poor, and the control stability of the unmanned vehicle is insufficient.

[0006] On the one hand, an embodiment of the present invention provides a method for controlling the speed limit of an unmanned vehicle when the communication quality decreases, characterized in that the method includes: Collect the status data of the unmanned vehicle in real time, and calculate the communication quality score of the unmanned vehicle based on the collected status data; Compare the communication quality score with the minimum standard score. If the communication quality score is greater than or equal to the minimum standard score, continue to collect the status data of the unmanned vehicle; if the communication quality score is less than the minimum standard score, control the unmanned vehicle to enter the weak communication mode; When the unmanned vehicle is in a weak communication mode, the communication signal strength of the unmanned vehicle is collected in real time, and the communication signal strength of the unmanned vehicle is compared with the first communication state threshold; When the communication signal strength of the unmanned vehicle is less than a first communication state threshold, the road condition data of the unmanned vehicle is collected in real time; and the speed limit of the unmanned vehicle is controlled based on the communication signal strength and road condition data of the unmanned vehicle.

[0007] As a further improvement of the present application, the speed limit control of the unmanned vehicle is performed based on the communication signal strength and road condition data of the unmanned vehicle, including: Input the road condition data and speed data of the unmanned vehicle into the pre-built speed adaptation model, and output the real-time speed limit of the unmanned vehicle; The current speed of the unmanned vehicle is obtained. Based on the real-time speed limit and the current speed of the unmanned vehicle, the adjusted acceleration of the unmanned vehicle is calculated through the acceleration smoothing algorithm, and the speed of the unmanned vehicle is controlled to the real-time speed limit accordingly.

[0008] As a further improvement of the present application, the speed adaptation model of the unmanned vehicle is shown in formula (1); (1); in, To limit speed in real time; Maximum speed for environmental conditions; The maximum speed of the unmanned vehicle; Maximum speed for safety conditions.

[0009] As a further improvement of the present application, the maximum speed under environmental conditions is calculated as shown in formula (2); ; in, Establishing maximum speed limits for roads; is the road surface humidity influencing factor; is the pavement temperature influencing factor; is the influencing factor of road roughness; , , are the weight coefficients of road surface humidity, road surface temperature, and road surface roughness respectively; The maximum speed of the unmanned vehicle is shown in formula (3); ; in, Designing the maximum speed for driverless cars; is the current vehicle acceleration; is the acceleration influence coefficient; The maximum speed under safety conditions is calculated as shown in formula (4); (4); in, is the current collision probability of the unmanned vehicle; is the collision probability threshold.

[0010] As a further improvement of the present application, based on the real-time speed limit and the current speed of the unmanned vehicle, the adjusted acceleration of the unmanned vehicle is calculated by an acceleration smoothing algorithm as shown in calculation formula (5); (5); in, Adjusting acceleration for driverless cars; The response time of the driverless car; is the maximum acceleration threshold; Real-time speed limit for driverless cars; is the current speed of the unmanned vehicle; is the restriction function.

[0011] As a further improvement of the present application, the method further includes: after the speed limit control is performed on the unmanned vehicle, if the current communication signal strength of the unmanned vehicle is greater than the first communication state threshold, then exiting the weak communication mode and releasing the speed limit control on the unmanned vehicle; If the current communication signal strength of the unmanned vehicle is less than or equal to the first communication state threshold and greater than or equal to the second communication state threshold, the speed limit control of the unmanned vehicle continues based on the current real-time speed limit; If the communication signal strength of the unmanned vehicle is less than the second communication state threshold, the unmanned vehicle is braked.

[0012] As a further improvement of the present application, the braking control of the unmanned vehicle includes: The current real-time speed limit is set to zero, the braking acceleration of the unmanned vehicle is calculated based on the calculation formula (5), and the braking control of the unmanned vehicle is performed based on the braking acceleration.

[0013] As a further improvement of the present application, real-time collection of status data of the unmanned vehicle, and calculation of the communication quality score of the unmanned vehicle based on the collected status data include: converting the collected status data into a corresponding status data score, and calculating the communication quality score of the unmanned vehicle based on the converted status data score; the status data of the unmanned vehicle includes communication type data, transmission power data, function level data, data transmission data, signal strength data and network status data; the status data score includes a communication type score, a transmission power score, a function level score, a data transmission score, a signal strength score and a network status score; The communication quality score of the unmanned vehicle is calculated as shown in formula (6); ; in, rate the quality of communications; Score the type of communication; Score the transmit power; Score the level of functionality; Score data transfers; Score signal strength; Score the network status; , , , , , They are the weights of communication type score, transmission power score, function level score, data transmission score, signal strength score, and network status score.

[0014] As a further improvement of the present application, the transmission power data includes actual transmission power and standard power, and the transmission power score is a ratio or a normalized value of the actual transmission power to the standard power; The signal strength data includes signal strength and signal-to-noise ratio. The signal strength score is shown in formula (7); ; in, is the signal strength; is the signal-to-noise ratio; is the theoretical maximum value of signal strength; is the theoretical maximum value of the signal-to-noise ratio; is the signal strength weight, is the signal-to-noise ratio weight; The network status includes network delay and packet loss rate; the network status score is as shown in calculation formula (8); ; in, Network delay; is the packet loss rate; is the network delay weight; is the packet loss rate weight.

[0015] As a further improvement of the present application, the method also includes dividing the collected status data into a first priority data set and a second priority data set, and when the unmanned vehicle is in a weak communication mode, only caching and transmitting the data in the first priority data set to the control terminal; wherein the first priority data set includes signal strength data and network status data, and the remaining status data is used as the second priority data set.

[0016] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects: 1. By collecting the status data of the unmanned vehicle in real time and calculating the communication quality score, it is possible to accurately monitor the changes in communication quality and promptly detect the decline in communication quality, thereby effectively improving the reliability and safety of the unmanned vehicle in communication. When the communication quality score is less than the minimum standard score, the system can respond quickly and enter the weak communication mode to avoid potential risks caused by communication problems.

[0017] 2. Speed ​​limit control is implemented on unmanned vehicles based on their communication signal strength and road condition data, so that the vehicle speed can be dynamically adjusted according to the real-time communication status and actual road environment. When the communication quality is poor, the vehicle speed is reduced in time to ensure that the vehicle travels within a controllable range. Even if problems such as communication delays occur, the possibility of accidents can be minimized, thereby improving the adaptability and safety of unmanned vehicles in complex communication environments.

[0018] 3. The speed adaptive model and acceleration smoothing algorithm are used to comprehensively consider multiple factors to determine the real-time speed limit and adjust the acceleration, making the speed adjustment smoother, ensuring the driving safety of the unmanned vehicle, ensuring that the speed is always in the optimal state, and improving the stability of the unmanned vehicle control.

[0019] In the present invention, the above-mentioned technical solutions can also be combined with each other to achieve more preferred combination solutions. Other features and advantages of the present invention will be described in the subsequent description, and some advantages can become obvious from the description, or can be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings are only used for the purpose of illustrating specific embodiments and are not to be considered as limiting the present invention. In the entire drawings, the same reference symbols represent the same components; Figure 1 A schematic flow chart of a method for controlling the speed limit of an unmanned vehicle when the communication quality decreases provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0022] A specific embodiment of the present invention discloses a method for controlling the speed limit of an unmanned vehicle when the communication quality decreases. Figure 1 The method comprises: Step 101, collect the status data of the unmanned vehicle in real time, and calculate the communication quality score of the unmanned vehicle based on the collected status data.

[0023] The unmanned vehicle status data is used to calculate the communication quality score of the unmanned vehicle. The unmanned vehicle status data can be collected in real time by sensors installed on the unmanned vehicle and transmitted to the on-board CPU. The on-board CPU calculates the communication quality score based on the collected unmanned vehicle status data.

[0024] Specifically, the status data of the unmanned vehicle includes communication type data, transmission power data, function level data, data transmission data, signal strength data and network status data; the status data score includes communication type score, transmission power score, function level score, data transmission score, signal strength score and network status score. Real-time collection of the status data of the unmanned vehicle and calculation of the communication quality score of the unmanned vehicle based on the collected status data include: converting the collected status data into a corresponding status data score, and calculating the communication quality score of the unmanned vehicle based on the converted status data score; the status data score includes communication type score, transmission power score, function level score, data transmission score, signal strength score and network status score; the calculation of the communication quality score of the unmanned vehicle is shown in formula (1); ; in, rate the quality of communications; Score the type of communication; Score the transmit power; Score the level of functionality; Score data transfers; Score signal strength; Score the network status; , , , , , The weights of the communication type score, transmission power score, function level score, data transmission score, signal strength score, and network status score can be obtained based on the unmanned vehicle operation experience, experimental tests, or actual operation data of the unmanned vehicle in different scenarios.

[0025] Specifically, the communication type score is a score assigned based on the adaptability of the current communication protocol or communication method of the unmanned vehicle, which can be quantified as an integer, such as LTE=3, 5G=5. The transmission power data includes the actual transmission power and the standard power. The transmission power score is the ratio or normalized value of the actual transmission power to the standard power. The function level data is the integrity of the current communication function. The function level score is 0-1. The closer the score is to 1, the higher the function integrity. Data transmission data is used to express the degree of concurrent support for voice and data transmission. The data transmission score sets measurement indicators for voice and data transmission respectively, such as voice clarity and coherence, and data transmission rate, packet loss rate, transmission delay, etc., sets weights according to their importance in the unmanned vehicle communication process, and obtains the data transmission score based on the weighted sum. The signal strength data includes signal strength and signal-to-noise ratio. The signal strength score is shown in formula (2); ; in, is the signal strength; is the signal-to-noise ratio; is the theoretical maximum value of signal strength; is the theoretical maximum value of the signal-to-noise ratio; is the signal strength weight, is the signal-to-noise ratio weight; network status data includes network delay and packet loss rate; The network status score is as shown in calculation formula (3); ; in, Network delay; is the packet loss rate; is the network delay weight; is the packet loss rate weight.

[0026] Step 102, compare the communication quality score with the minimum standard score. If the communication quality score is greater than or equal to the minimum standard score, continue to collect the unmanned vehicle status data; if the communication quality score is less than the minimum standard score, control the unmanned vehicle to enter the weak communication mode.

[0027] The minimum standard score can be a threshold for the communication quality score of the unmanned vehicle set in advance through experimental testing, simulation, and analysis of the operating status of the unmanned vehicle in different scenarios. If the communication quality score is greater than or equal to the minimum standard score, the communication quality of the unmanned vehicle can meet the communication requirements and the vehicle status data can continue to be collected normally. If the communication quality score is less than the minimum standard score, the communication quality of the unmanned vehicle cannot meet the normal communication requirements and the unmanned vehicle needs to be controlled to enter the weak communication mode.

[0028] In weak communication mode, due to the current decline in communication quality of the unmanned vehicle, only the data in the first priority data set is cached and transmitted to the control terminal. According to the importance of data to the safe operation, basic control and avoidance of serious safety accidents of the unmanned vehicle, the collected unmanned vehicle status data is divided into two priorities, namely the first priority data set and the second priority data set. In weak communication mode, only the data in the first priority data set is cached and transmitted to the control terminal to ensure that the unmanned vehicle can maintain basic functions and ensure basic safety when the communication quality declines. The first priority data set includes signal strength data and network status data, and the remaining status data is the second priority data set.

[0029] Furthermore, when the unmanned vehicle is in weak communication mode, the reminder function of the remote control end of the unmanned vehicle will be triggered. The reminder function is realized by flashing lights and sound prompts. On the control end, a special status indicator light is set. When entering the weak communication mode, the control end receives the corresponding signal from the on-board CPU, so that the indicator light starts flashing. The flashing can be periodic, such as alternating on and off, so as to attract the attention of the operator. In addition, the control end will also start the sound reminder mechanism, using the set buzzer or speaker to send out a specific sound signal, such as a continuous buzzing sound or a periodic prompt sound. Through this visual and auditory reminder, the remote control end can quickly detect the communication problems of the unmanned vehicle, and then pay attention to the operating status of the unmanned vehicle, and take corresponding operations or intervention measures when necessary to ensure the safety and stable operation of the unmanned vehicle in the weak communication mode.

[0030] Step 103, when the unmanned vehicle is in a weak communication mode, the communication signal strength of the unmanned vehicle is collected in real time, and the communication signal strength of the unmanned vehicle is compared with the first communication state threshold.

[0031] When the unmanned vehicle is in a weak communication mode, the communication signal strength of the unmanned vehicle can be collected in real time through a signal strength detection circuit or other radio frequency signal strength sensors, and the current communication signal strength can be compared with the first communication state threshold.

[0032] Step 104, when the communication signal strength of the unmanned vehicle is less than the first communication state threshold, the road condition data of the unmanned vehicle is collected in real time; and the speed limit of the unmanned vehicle is controlled based on the communication signal strength of the unmanned vehicle and the road condition data.

[0033] Specifically, the speed limit control of the unmanned vehicle is performed based on the speed data of the unmanned vehicle and the road condition data, including: Step 1041, input the road condition data and speed data of the unmanned vehicle into a pre-built speed adaptation model, and output the real-time speed limit of the unmanned vehicle.

[0034] Road condition data includes the maximum speed limit specified on the road, road surface humidity data, road surface temperature data, and road surface roughness data. Road condition data reflects the actual condition of the road. Wet roads will reduce the friction between tires and the ground, high temperature roads will affect the performance of vehicle components, and rough roads will affect the stability of vehicle driving, thereby affecting the safe driving speed of the vehicle. The maximum speed limit specified on the road can be obtained by obtaining traffic signs on the road in advance or by image recognition. Road surface humidity data can be collected by humidity sensors, road surface temperature data can be obtained by contact temperature sensors or non-contact infrared temperature sensors, and road surface roughness data can be collected by laser profilers or lidars. Unmanned vehicle speed data includes the maximum speed designed for unmanned vehicles and the current vehicle acceleration. The current acceleration of the unmanned vehicle can be obtained by an acceleration sensor or a wheel speed sensor.

[0035] Specifically, the speed adaptation model of the unmanned vehicle is shown in equation (4); (4); in, To limit speed in real time; Maximum speed for environmental conditions; The maximum speed of the unmanned vehicle; The maximum speed under safety conditions. The maximum speed under environmental conditions refers to the maximum speed under environmental conditions obtained by combining the maximum speed limit requirements set by traffic rules or road design with the road environment. The maximum speed under unmanned vehicle status is the maximum speed determined by the design performance of the vehicle under ideal conditions. The maximum speed under safety conditions is the maximum speed allowed when the unmanned vehicle is operating safely, and is a speed limit value calculated based on the current collision probability of the unmanned vehicle.

[0036] The maximum speed under environmental conditions is calculated as shown in formula (5); ; in, Establishing maximum speed limits for roads; is the road surface humidity influencing factor; is the pavement temperature influencing factor; is the influencing factor of road roughness; , , are the road surface humidity, road surface temperature, and road surface roughness weight coefficients, respectively. The maximum speed of the unmanned vehicle is shown in formula (6); ; in, Designing the maximum speed for driverless cars; is the current vehicle acceleration; is the acceleration influence coefficient; The maximum speed under safety conditions is calculated as shown in formula (7); ; in, is the current collision probability of the unmanned vehicle; is the collision probability threshold. The current collision probability is the probability of the unmanned vehicle colliding with other obstacles (such as pedestrians, vehicles, obstacles, etc.) at the current moment, combined with its own state and surrounding environment information. The unmanned vehicle is equipped with a variety of environmental perception sensors, such as lidar, camera, millimeter wave radar, ultrasonic sensor, etc. During the driving process of the unmanned vehicle, the environmental information around the unmanned vehicle is continuously collected through the environmental perception sensor. The calculation of the current collision probability of the unmanned vehicle can be calculated by the relative position, speed, acceleration, motion trajectory and other information of the unmanned vehicle and the surrounding objects, using an algorithm based on geometric relationships and kinematics to calculate the collision probability, which belongs to the existing technology. For example, the collision probability can be calculated based on the distance and speed between the objects, combined with the braking distance and reaction time of the vehicle, using a probability model (such as a Gaussian distribution model). If the relative distance between the unmanned vehicle and the target object is getting smaller and smaller, and the relative speed direction is pointing to each other, the collision probability will increase.

[0037] The collision probability threshold is a pre-set critical value. When the current collision probability of the unmanned vehicle exceeds the collision probability threshold, the unmanned vehicle is considered to be at a high collision risk state and needs to take corresponding safety measures, such as reducing speed. The collision probability threshold can be set based on safety standards, traffic regulations, and a large number of experimental and test results.

[0038] Step 1042, obtain the current speed of the unmanned vehicle, calculate the adjusted acceleration of the unmanned vehicle through an acceleration smoothing algorithm based on the real-time speed limit and the current speed of the unmanned vehicle, and control the speed of the unmanned vehicle to the real-time speed limit accordingly.

[0039] Based on the real-time speed limit and the current speed of the unmanned vehicle, the adjusted acceleration of the unmanned vehicle is calculated by the acceleration smoothing algorithm as shown in formula (8); ; in, Adjusting acceleration for driverless cars; The response time of the driverless car; is the maximum acceleration threshold; Real-time speed limit for driverless cars; is the current speed of the unmanned vehicle; is the limiting function. The response time of the unmanned vehicle refers to the time required for the unmanned vehicle to actually change its speed after receiving the speed adjustment instruction. The maximum acceleration threshold is the absolute value of the maximum acceleration allowed when the unmanned vehicle is adjusting its speed.

[0040] Furthermore, the method further comprises: After the speed limit control is performed on the unmanned vehicle, if the current communication signal strength of the unmanned vehicle is greater than the first communication state threshold, the weak communication mode is exited and the speed limit control on the unmanned vehicle is released; If the current communication signal strength of the unmanned vehicle is less than or equal to the first communication state threshold and greater than or equal to the second communication state threshold, the speed limit control of the unmanned vehicle continues based on the current real-time speed limit; If the communication signal strength of the unmanned vehicle is less than the second communication state threshold, the unmanned vehicle is braked. When the communication signal strength of the unmanned vehicle is lower than the threshold, it indicates that the communication condition is very bad, which may seriously affect the normal operation and safety of the unmanned vehicle. Therefore, braking control is required to stop the unmanned vehicle as soon as possible to avoid more serious safety accidents caused by poor communication, such as collision, loss of control, etc.

[0041] Braking control of unmanned vehicles includes: The current real-time speed limit is set to zero, the braking acceleration of the unmanned vehicle is calculated based on the calculation formula (8), and the braking control of the unmanned vehicle is performed based on the braking acceleration.

[0042] Compared with the prior art, the present embodiment provides a method for controlling the speed limit of an unmanned vehicle when the communication quality decreases. By real-time collection of the unmanned vehicle status data and calculation of the communication quality score, the change of the communication quality can be accurately monitored, and the situation of the communication quality decrease can be discovered in time, thereby effectively improving the reliability and safety of the unmanned vehicle in communication. When the communication quality score is less than the minimum standard score, the system can respond quickly and enter the weak communication mode to avoid potential risks caused by communication problems. The speed limit of the unmanned vehicle is controlled based on the communication signal strength and road condition data of the unmanned vehicle, so that the vehicle speed can be dynamically adjusted according to the real-time communication status and the actual road environment. When the communication quality is poor, the vehicle speed is reduced in time to ensure that the vehicle travels within a controllable range. Even if problems such as communication delays occur, the possibility of accidents can be minimized, thereby improving the adaptability and safety of the unmanned vehicle in a complex communication environment. The speed adaptive model and the acceleration smoothing algorithm are adopted to comprehensively determine the real-time speed limit and adjust the acceleration based on multiple factors, so that the adjustment of the vehicle speed is smoother, the driving safety of the unmanned vehicle is guaranteed, the vehicle speed is always in the optimal state, and the stability of the unmanned vehicle control is improved.

[0043] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0044] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for controlling the speed limit of an unmanned vehicle when the communication quality decreases, characterized in that: The method comprises: Collect the status data of the unmanned vehicle in real time, and calculate the communication quality score of the unmanned vehicle based on the collected status data; Compare the communication quality score with the minimum standard score. If the communication quality score is greater than or equal to the minimum standard score, continue to collect the status data of the unmanned vehicle; if the communication quality score is less than the minimum standard score, control the unmanned vehicle to enter the weak communication mode; When the unmanned vehicle is in a weak communication mode, the communication signal strength of the unmanned vehicle is collected in real time, and the communication signal strength of the unmanned vehicle is compared with the first communication state threshold; When the communication signal strength of the unmanned vehicle is less than a first communication state threshold, speed data and road condition data of the unmanned vehicle are collected in real time; and speed limit control of the unmanned vehicle is performed based on the speed data and road condition data of the unmanned vehicle.

2. The method according to claim 1, characterized in that Speed ​​limit control of unmanned vehicles is performed based on the communication signal strength and road condition data of the unmanned vehicle, including: Input the road condition data and speed data of the unmanned vehicle into the pre-built speed adaptation model, and output the real-time speed limit of the unmanned vehicle; The current speed of the unmanned vehicle is obtained. Based on the real-time speed limit and the current speed of the unmanned vehicle, the adjusted acceleration of the unmanned vehicle is calculated through the acceleration smoothing algorithm, and the speed of the unmanned vehicle is controlled to the real-time speed limit accordingly.

3. The method according to claim 2, characterized in that The speed adaptation model of the unmanned vehicle is shown in formula (1); （1）； in, To limit speed in real time; Maximum speed for environmental conditions; The maximum speed of the unmanned vehicle; Maximum speed for safety conditions.

4. The method according to claim 3, characterized in that The maximum speed under environmental conditions is shown in formula (2); ； in, Establishing maximum speed limits for roads; is the road surface humidity influencing factor; is the pavement temperature influencing factor; is the influencing factor of road roughness; , , are the weight coefficients of road surface humidity, road surface temperature, and road surface roughness respectively; The maximum speed of the unmanned vehicle is shown in formula (3); ； in, Designing the maximum speed for driverless cars; is the current vehicle acceleration; is the acceleration influence coefficient; The maximum speed under safety conditions is calculated as shown in formula (4); （4）； in, is the current collision probability of the unmanned vehicle; is the collision probability threshold.

5. The method according to claim 4, characterized in that Based on the real-time speed limit and the current speed of the unmanned vehicle, the adjusted acceleration of the unmanned vehicle is calculated by the acceleration smoothing algorithm as shown in formula (5); （5）； in, Adjusting acceleration for driverless cars; The response time of the driverless car; is the maximum acceleration threshold; Real-time speed limit for driverless cars; is the current speed of the unmanned vehicle; is the restriction function.

6. The method according to claim 5, characterized in that The method further includes: after the speed limit control is performed on the unmanned vehicle, if the current communication signal strength of the unmanned vehicle is greater than the first communication state threshold, then exiting the weak communication mode and releasing the speed limit control on the unmanned vehicle; If the current communication signal strength of the unmanned vehicle is less than or equal to the first communication state threshold and greater than or equal to the second communication state threshold, the speed limit control of the unmanned vehicle continues based on the current real-time speed limit; If the communication signal strength of the unmanned vehicle is less than the second communication state threshold, the unmanned vehicle is braked.

7. The method according to claim 6, characterized in that Braking control of unmanned vehicles includes: The current real-time speed limit is set to zero, the braking acceleration of the unmanned vehicle is calculated based on the calculation formula (5), and the braking control of the unmanned vehicle is performed based on the braking acceleration.

8. The method according to claim 1, characterized in that Collecting the status data of the unmanned vehicle in real time, and calculating the communication quality score of the unmanned vehicle based on the collected status data includes: converting the collected status data into a corresponding status data score, and calculating the communication quality score of the unmanned vehicle based on the converted status data score; the status data of the unmanned vehicle includes communication type data, transmission power data, function level data, data transmission data, signal strength data and network status data; the status data score includes communication type score, transmission power score, function level score, data transmission score, signal strength score and network status score; The communication quality score of the unmanned vehicle is calculated as shown in formula (6); ； in, rate the quality of communications; Score the type of communication; Score the transmit power; Score the level of functionality; Score data transfers; Score signal strength; Score the network status; , , , , , They are the weights of communication type score, transmission power score, function level score, data transmission score, signal strength score, and network status score.

9. The method according to claim 8, characterized in that The transmission power data includes actual transmission power and standard power, and the transmission power score is the ratio or normalized value of the actual transmission power to the standard power; The signal strength data includes signal strength and signal-to-noise ratio. The signal strength score is shown in formula (7); ； in, is the signal strength; is the signal-to-noise ratio; is the theoretical maximum value of signal strength; is the theoretical maximum value of the signal-to-noise ratio; is the signal strength weight, is the signal-to-noise ratio weight; The network status includes network delay and packet loss rate; the network status score is as shown in calculation formula (8); ； in, Network delay; is the packet loss rate; is the network delay weight; is the packet loss rate weight.

10. The method according to claim 8, characterized in that The method also includes dividing the collected status data into a first priority data set and a second priority data set, and when the unmanned vehicle is in a weak communication mode, only caching and transmitting the data in the first priority data set to the control terminal; wherein the first priority data set includes signal strength data and network status data, and the remaining status data is used as the second priority data set.

Images