Human-computer interaction method and system of unmanned vehicle

Abstract

The invention discloses a man-machine interaction method and system for an unmanned vehicle, and particularly relates to the technical field of unmanned driving. Comprising an unmanned vehicle driving path division module, an unmanned vehicle driving road condition information acquisition module, an unmanned vehicle driving power control module, an unmanned vehicle road condition information analysis module, an unmanned vehicle safety performance module and an unmanned vehicle safety performance evaluation module. According to the invention, the driving road condition data of each monitoring sub-path in the corresponding driving path of the target unmanned vehicle is collected, and the driving power control index of the unmanned vehicle is calculated, so that the vehicle driving power abnormity management is carried out, the manual intervention is reduced, the man-machine interaction convenience is improved, and the working efficiency is improved. The road condition information optimization index of the driving road of the unmanned vehicle is calculated, the driving safety coefficient of the unmanned vehicle is obtained through analysis, the road driving safety performance of the unmanned vehicle can be comprehensively monitored, and the safety of the unmanned vehicle is improved.

Description

Technical Field

[0001] The present invention relates to the field of unmanned driving technology, and more specifically, to a human-computer interaction method and an interaction system for an unmanned driving vehicle. Background Art

[0002] Unmanned vehicles, also known as autonomous vehicles, are the product of multiple disciplines and technologies such as integrated vehicle engineering, artificial intelligence, computer vision, sensor technology, automatic control, map positioning and navigation. The safety of unmanned vehicles depends to a large extent on the accuracy and reliability of their sensor technology. Sensors collect information about the surrounding environment, including other vehicles, pedestrians, road signs and potential obstacles. The real-time processing and analysis of data generated by these sensors is crucial for vehicles to make quick and accurate decisions, thereby providing strong guarantees for the safe driving of vehicles.

[0003] At present, driverless technology has been commercialized in many fields, including driverless taxis, buses, road sweepers, logistics delivery vehicles, etc. These applications have effectively improved traffic efficiency and reduced labor costs. With the continuous advancement of autonomous driving technology, human-computer interaction mode has become one of the key factors to ensure driving safety. The naturalness and adaptability of interaction are the key to enhancing user experience and ensuring safety.

[0004] However, there are still some shortcomings in its actual use. For example, the existing unmanned driving technology is not trustworthy enough in abnormal environments, including in bad weather conditions. The human-computer interaction algorithm analysis is not in-depth enough, which affects the perception ability of unmanned vehicles to drive safely. Another safety hazard of existing driverless vehicles is the uncertainty and delay in the fusion processing results of the on-board communication data transmitted by sensors, which reduces the efficiency of human-computer interaction to a certain extent. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a human-computer interaction method and an interaction system for an unmanned vehicle, which are used to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a human-computer interaction system for an unmanned vehicle, comprising: Unmanned vehicle driving path division module: used to divide the driving path of the target unmanned vehicle into monitoring sub-paths of equal route length, and number each monitoring sub-path in the corresponding driving path of the target unmanned vehicle.

[0007] Unmanned vehicle driving road condition information collection module: used to collect the driving road condition data of each monitoring sub-path in the corresponding driving path of the target unmanned vehicle through the vehicle laser sensor. The unmanned vehicle driving road condition information collection module includes a vehicle driving power perception unit and a vehicle driving road condition perception unit.

[0008] Unmanned vehicle driving power control module: used to calculate the unmanned vehicle driving power control index of each monitoring sub-path in the corresponding driving path of the target unmanned vehicle based on the vehicle driving power perception data of the vehicle driving power perception unit, and convey the driving route of the vehicle with abnormal power.

[0009] Unmanned vehicle road condition information analysis module: used to calculate the unmanned vehicle driving performance indicators and unmanned vehicle driving sensor perception indicators of each monitoring sub-path based on the vehicle driving condition perception data of the vehicle driving road condition perception unit, and analyze to obtain the unmanned vehicle driving road condition information optimization index of each monitoring sub-path in the corresponding driving path of the target unmanned vehicle.

[0010] Unmanned vehicle safety performance module: used to calculate the unmanned vehicle driving safety factor of each monitored sub-path in the corresponding driving path of the target unmanned vehicle.

[0011] Unmanned vehicle safety performance evaluation module: used to obtain the unmanned vehicle driving safety factor of each monitoring sub-path in the corresponding driving path of the target unmanned vehicle, compare it with the preset unmanned vehicle driving safety factor, and process it.

[0012] Preferably, the unmanned vehicle driving path division module is specifically: The driving path of the unmanned vehicle is determined through map information, and is divided into monitoring sub-paths according to the division method of equal route length, and the monitoring sub-paths in the driving path corresponding to the target unmanned vehicle are numbered 1, 2, ...i, ...n in sequence.

[0013] Preferably, the unmanned vehicle driving road condition information collection module is specifically: Vehicle driving dynamics sensing unit: used to collect the wheel track, vehicle center of mass height, vehicle road slope, and vehicle air resistance of each monitoring sub-path in the corresponding driving path of the target unmanned vehicle through the vehicle laser sensor, which are marked as , , , , where i=1,2,...n, i represents the number of the i-th monitoring subpath; Vehicle road condition perception unit: used to collect the rolling resistance of the vehicle wheels, vehicle speed, vehicle sway angular velocity, vehicle light intensity, vehicle road surface humidity, and vehicle throttle opening of each monitoring sub-path in the corresponding driving path of the target unmanned vehicle through the vehicle laser sensor, which are marked as , , , , , .

[0014] Preferably, the unmanned vehicle driving power control module is specifically: Step S41:

[0015] in, It is represented as the driving dynamics control index of the unmanned vehicle in the ith monitoring subpath, It is represented as the vehicle air resistance of the i-th monitoring subpath, It is represented by the wheel track of the vehicle in the ith monitoring subpath, It is represented as the height of the vehicle center of mass of the ith monitoring subpath, It is represented as the road slope of the vehicle in the ith monitoring subpath; Step S42: Compare the unmanned vehicle driving power control index of each monitored sub-path in the corresponding driving path of the target unmanned vehicle with the preset unmanned vehicle driving power control index. If the unmanned vehicle driving power control index of a sub-path is greater than the preset unmanned vehicle driving power control index, it indicates that there is an abnormality in the vehicle power of the unmanned vehicle driving route, and the management personnel should be notified through the on-board communication network to initiate processing measures. Otherwise, it indicates that there is no abnormality in the vehicle power of the driving route.

[0016] Preferably, the unmanned vehicle road condition information analysis module is specifically: Step S51: Calculate the unmanned vehicle driving performance index of each monitoring sub-path through the rolling resistance of the vehicle wheels, the vehicle driving speed, and the vehicle driving yaw angular velocity:

[0017] in, It is represented as the driving performance index of the unmanned vehicle on the ith monitoring subpath, It is represented by the rolling resistance of the vehicle wheels in the i-th monitoring subpath, It is represented as the vehicle speed of the i-th monitoring subpath, Indicates the preset vehicle speed. It is represented as the vehicle sway angular velocity of the i-th monitoring subpath, It is expressed as the vehicle sway angular velocity of the i-1th monitoring subpath, It is expressed as the maximum value of the vehicle's yaw angular velocity. It is expressed as the minimum value of the vehicle's sway angular velocity; Step S52: Calculate the sensor perception index of the unmanned vehicle driving on each monitoring sub-path according to the vehicle driving light intensity, vehicle driving road humidity, and vehicle driving throttle opening:

[0018] in, It is represented as the sensor perception index of the unmanned vehicle driving on the i-th monitoring subpath, It is represented by the vehicle driving light intensity of the ith monitoring subpath, Represents the preset vehicle driving light intensity, It is represented by the road surface humidity of the vehicle in the ith monitoring subpath, Indicated as the preset vehicle driving road humidity, It is represented by the throttle opening fluctuation of the vehicle in the i-th monitoring subpath, It is represented by the throttle opening fluctuation of the vehicle in the i-1th monitoring subpath. It is represented as a natural constant, and n is the number of monitored subpaths; Step S53: The calculation formula of the optimization index of the road condition information of the unmanned vehicle is:

[0019] in, It is represented as the optimization index of the road condition information of the unmanned vehicle on the i-th monitoring sub-path, It is expressed as the minimum value of the driving performance index of the unmanned vehicle. It is expressed as the maximum value of the driving performance index of the unmanned vehicle. It is expressed as the minimum value of the sensor perception index of the unmanned vehicle. It is expressed as the maximum value of the sensor perception index of the unmanned vehicle.

[0020] Preferably, the unmanned vehicle safety performance module is specifically: Step S61: Obtain the wind speed of the target unmanned vehicle during driving on that day, marked as ; Step S62: The calculation formula of the driving safety factor of the unmanned vehicle is:

[0021] in, It is represented as the driving safety factor of the unmanned vehicle in the ith monitoring subpath, Expressed as wind speed, It is represented as the driving dynamics control index of the unmanned vehicle in the ith monitoring subpath, It is represented as the vehicle speed of the i-th monitoring subpath, Indicates the preset vehicle speed. It is represented as the throttle opening of the vehicle in the i-th monitoring subpath, It is represented as the optimization index of the road condition information of the unmanned vehicle on the i-th monitoring sub-path.

[0022] Preferably, the unmanned vehicle safety performance evaluation module is specifically: Obtain the unmanned vehicle driving safety factor of each monitoring sub-path in the corresponding driving path of the target unmanned vehicle, and compare it with the preset unmanned vehicle driving safety factor. If the unmanned vehicle driving safety factor of a certain monitoring sub-path is less than the preset unmanned vehicle driving safety factor, it indicates that the road driving of the unmanned vehicle on this sub-path does not meet the safety standards, and the current geographical location of the vehicle should be sent to the management personnel through the vehicle communication network. Otherwise, it indicates that the road driving of the unmanned vehicle on this sub-path meets the safety standards.

[0023] Preferably, a human-computer interaction method for an unmanned vehicle comprises the following steps: Step S01: unmanned vehicle driving path division: used to divide the driving path of the target unmanned vehicle into monitoring sub-paths of equal route length, and number each monitoring sub-path in the corresponding driving path of the target unmanned vehicle; Step S02: Unmanned vehicle driving road condition information collection: used to collect driving road condition data of each monitoring sub-path in the driving path corresponding to the target unmanned vehicle through a vehicle laser sensor, and the unmanned vehicle driving road condition information collection includes a vehicle driving power sensing unit and a vehicle driving road condition sensing unit; Step S03: unmanned vehicle driving power control: used to calculate the unmanned vehicle driving power control index of each monitoring sub-path in the driving path corresponding to the target unmanned vehicle according to the vehicle driving power sensing data of the vehicle driving power sensing unit, and convey the driving route of the vehicle with abnormal power; Step S04: unmanned vehicle road condition information analysis: used to calculate the unmanned vehicle driving performance index and the unmanned vehicle driving sensor perception index of each monitoring sub-path according to the vehicle driving road condition perception data of the vehicle driving road condition perception unit, and analyze and obtain the unmanned vehicle driving road condition information optimization index of each monitoring sub-path in the driving path corresponding to the target unmanned vehicle; Step S05: Unmanned vehicle safety performance: used to calculate the unmanned vehicle driving safety factor of each monitoring sub-path in the driving path corresponding to the target unmanned vehicle; Step S06: Unmanned vehicle safety performance evaluation: used to obtain the unmanned vehicle driving safety factor of each monitored sub-path in the corresponding driving path of the target unmanned vehicle, compare it with the preset unmanned vehicle driving safety factor, and process it.

[0024] Technical effects and advantages of the present invention: The present invention provides a human-machine interaction method and interactive system for unmanned vehicles. The vehicle laser sensor is used to collect the driving road condition data of each monitoring sub-path in the driving path corresponding to the target unmanned vehicle. According to the vehicle driving power perception data of the vehicle driving power perception unit, the unmanned vehicle driving power control index of each monitoring sub-path in the driving path corresponding to the target unmanned vehicle is calculated and compared with the preset unmanned vehicle driving power control index. If the unmanned vehicle driving power control index of a certain sub-path is greater than the preset unmanned vehicle driving power control index, it indicates that the vehicle power of the driving route of the unmanned vehicle is abnormal, and the management personnel should be notified through the vehicle communication network to start the processing measures. Otherwise, it indicates that there is no abnormality in the vehicle power of the driving route. By continuously monitoring and analyzing the vehicle driving power data, the driving state of the unmanned vehicle can be evaluated in real time, reducing the need for manual intervention of the unmanned vehicle. At the same time, by judging the abnormality, it is conducive to timely adjusting the driving state of the unmanned vehicle, and improving the convenience of human-machine interaction decision-making. The present invention provides a human-machine interaction method and interactive system for unmanned vehicles. The vehicle laser sensor is used to collect the driving road condition data of each monitoring sub-path in the driving path corresponding to the target unmanned vehicle. The driving road condition data of the sub-path is used to calculate the unmanned vehicle driving performance index and the unmanned vehicle driving sensor perception index of each monitoring sub-path according to the vehicle driving road condition perception data of the vehicle driving road condition perception unit. The unmanned vehicle driving road condition information optimization index of each monitoring sub-path in the driving path corresponding to the target unmanned vehicle is further analyzed to obtain the unmanned vehicle driving road condition information optimization index, so as to calculate the unmanned vehicle driving safety factor of each monitoring sub-path in the driving path corresponding to the target unmanned vehicle, and compare it with the preset unmanned vehicle driving safety factor. If the unmanned vehicle driving safety factor of a certain monitoring sub-path is less than the preset unmanned vehicle driving safety factor, it indicates that the road driving of the unmanned vehicle on this sub-path does not meet the safety standard, and the current geographical location of the vehicle should be sent to the management personnel through the vehicle communication network. Otherwise, it indicates that the road driving of the unmanned vehicle on this sub-path meets the safety standard. Through in-depth analysis of the data, the calculation accuracy of the driving safety of the unmanned vehicle on different routes is improved, which is conducive to comprehensive monitoring of the road driving safety of unmanned vehicles. Through early warning of the driving safety of unmanned vehicles, it is conducive to reducing the probability of unmanned vehicle accidents and improving the safety of unmanned vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a schematic diagram of the system module process connection of the present invention.

[0026] Figure 2 It is a schematic diagram of the structure of the unmanned vehicle road condition information collection module of the present invention.

[0027] Figure 3 It is a schematic diagram of the connection of the method steps of the present invention. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] See also Figure 1 As shown, the present invention provides a human-computer interaction system for an unmanned vehicle, including an unmanned vehicle driving path division module, an unmanned vehicle driving road condition information collection module, an unmanned vehicle driving power control module, an unmanned vehicle road condition information analysis module, an unmanned vehicle safety performance module, and an unmanned vehicle safety performance evaluation module.

[0030] The unmanned vehicle driving path division module is connected to the unmanned vehicle driving road condition information collection module, the unmanned vehicle driving road condition information collection module is connected to the unmanned vehicle driving power control module and the unmanned vehicle road condition information analysis module, the unmanned vehicle driving power control module and the unmanned vehicle road condition information analysis module are connected to the unmanned vehicle safety performance module, and the unmanned vehicle safety performance module is connected to the unmanned vehicle safety performance evaluation module.

[0031] The unmanned vehicle driving path division module is used to divide the driving path of the target unmanned vehicle into monitoring sub-paths of equal route length, and number each monitoring sub-path in the corresponding driving path of the target unmanned vehicle.

[0032] In a possible design, the unmanned vehicle driving path division module is specifically: The driving path of the unmanned vehicle is determined through map information, and is divided into monitoring sub-paths according to the division method of equal route length, and the monitoring sub-paths in the driving path corresponding to the target unmanned vehicle are numbered 1, 2, ...i, ...n in sequence.

[0033] See also Figure 2 As shown, the unmanned vehicle driving road condition information collection module is used to collect the driving road condition data of each monitoring sub-path in the corresponding driving path of the target unmanned vehicle through a vehicle laser sensor. The unmanned vehicle driving road condition information collection module includes a vehicle driving power perception unit and a vehicle driving road condition perception unit. The driving road condition data includes vehicle driving power perception data and vehicle driving road condition perception data.

[0034] In a possible design, the unmanned vehicle driving road condition information collection module is specifically: Vehicle driving dynamics sensing unit: used to collect the wheel track, vehicle center of mass height, vehicle road slope, and vehicle air resistance of each monitoring sub-path in the corresponding driving path of the target unmanned vehicle through the vehicle laser sensor, which are marked as , , , , where i=1,2,...n, i represents the number of the i-th monitoring subpath; Vehicle road condition perception unit: used to collect the rolling resistance of the vehicle wheels, vehicle speed, vehicle sway angular velocity, vehicle light intensity, vehicle road surface humidity, and vehicle throttle opening of each monitoring sub-path in the corresponding driving path of the target unmanned vehicle through the vehicle laser sensor, which are marked as , , , , , .

[0035] The unmanned vehicle driving power control module is used to calculate the unmanned vehicle driving power control index of each monitoring sub-path in the corresponding driving path of the target unmanned vehicle based on the vehicle driving power perception data of the vehicle driving power perception unit, and convey the vehicle driving route with abnormal power to the management personnel.

[0036] In a possible design, the calculation formula of the driving power control index of the unmanned vehicle is: Step S41:

[0037] in, It is represented as the driving dynamics control index of the unmanned vehicle in the ith monitoring subpath, It is represented as the vehicle air resistance of the i-th monitoring subpath, It is represented by the wheel track of the vehicle in the ith monitoring subpath, It is represented as the height of the vehicle center of mass of the ith monitoring subpath, It is represented as the road slope of the vehicle in the ith monitoring subpath; Step S42: Compare the unmanned vehicle driving power control index of each monitored sub-path in the corresponding driving path of the target unmanned vehicle with the preset unmanned vehicle driving power control index. If the unmanned vehicle driving power control index of a sub-path is greater than the preset unmanned vehicle driving power control index, it indicates that there is an abnormality in the vehicle power of the unmanned vehicle driving route, and the management personnel should be notified through the on-board communication network to initiate processing measures. Otherwise, it indicates that there is no abnormality in the vehicle power of the driving route.

[0038] The unmanned vehicle road condition information analysis module is used to calculate the unmanned vehicle driving performance index and the unmanned vehicle driving sensor perception index of each monitoring sub-path according to the vehicle driving road condition perception data of the vehicle driving road condition perception unit, and further analyze to obtain the unmanned vehicle driving road condition information optimization index of each monitoring sub-path in the corresponding driving path of the target unmanned vehicle.

[0039] In a possible design, the unmanned vehicle road condition information analysis module is specifically: Step S51: Calculate the unmanned vehicle driving performance index of each monitoring sub-path through the rolling resistance of the vehicle wheels, the vehicle driving speed, and the vehicle driving yaw angular velocity:

[0040] in, It is represented as the driving performance index of the unmanned vehicle on the ith monitoring subpath, It is represented by the rolling resistance of the vehicle wheels in the i-th monitoring subpath, It is represented as the vehicle speed of the i-th monitoring subpath, Indicates the preset vehicle speed. It is represented as the vehicle sway angular velocity of the i-th monitoring subpath, It is expressed as the vehicle sway angular velocity of the i-1th monitoring subpath, It is expressed as the maximum value of the vehicle's yaw angular velocity. It is expressed as the minimum value of the vehicle's sway angular velocity; Step S52: Calculate the sensor perception index of the unmanned vehicle driving on each monitoring sub-path according to the vehicle driving light intensity, vehicle driving road humidity, and vehicle driving throttle opening:

[0041] in, It is represented as the sensor perception index of the unmanned vehicle driving on the i-th monitoring subpath, It is represented by the vehicle driving light intensity of the ith monitoring subpath, Represents the preset vehicle driving light intensity, It is represented by the road surface humidity of the vehicle in the ith monitoring subpath, Indicated as the preset vehicle driving road humidity, It is represented by the throttle opening fluctuation of the vehicle in the i-th monitoring subpath, It is represented by the throttle opening fluctuation of the vehicle in the i-1th monitoring subpath. It is represented as a natural constant, and n is the number of monitored subpaths; Step S53: The calculation formula of the optimization index of the road condition information of the unmanned vehicle is:

[0042] in, It is represented as the optimization index of the road condition information of the unmanned vehicle on the i-th monitoring sub-path, It is expressed as the minimum value of the driving performance index of the unmanned vehicle. It is expressed as the maximum value of the driving performance index of the unmanned vehicle. It is expressed as the minimum value of the sensor perception index of the unmanned vehicle. It is expressed as the maximum value of the sensor perception index of the unmanned vehicle.

[0043] In this embodiment, it should be specifically explained that the calculation formula for the vehicle driving throttle opening fluctuation is:

[0044] in, It is represented by the throttle opening fluctuation of the vehicle in the i-th monitoring subpath, It is represented as the throttle opening of the vehicle in the i-th monitoring subpath, It is represented by the throttle opening of the vehicle in the i-1th monitoring subpath. It is expressed as the mean value of the vehicle's throttle opening. .

[0045] The unmanned vehicle safety performance module is used to calculate the unmanned vehicle driving safety factor of each monitored sub-path in the driving path corresponding to the target unmanned vehicle based on the unmanned vehicle driving power control index of each monitored sub-path in the driving path corresponding to the target unmanned vehicle and the unmanned vehicle driving road condition information optimization index.

[0046] In a possible design, the unmanned vehicle safety performance module is specifically: Step S61: Obtain the wind speed of the target unmanned vehicle during driving on that day, marked as ; Step S62: The calculation formula of the driving safety factor of the unmanned vehicle is:

[0047] in, It is represented as the driving safety factor of the unmanned vehicle in the ith monitoring subpath, Expressed as wind speed, It is represented as the driving dynamics control index of the unmanned vehicle in the ith monitoring subpath, It is represented as the vehicle speed of the i-th monitoring subpath, Indicates the preset vehicle speed. It is represented as the throttle opening of the vehicle in the i-th monitoring subpath, It is represented as the optimization index of the road condition information of the unmanned vehicle on the i-th monitoring sub-path.

[0048] The unmanned vehicle safety performance evaluation module is used to obtain the unmanned vehicle driving safety factor of each monitoring sub-path in the driving path corresponding to the target unmanned vehicle, compare it with the preset unmanned vehicle driving safety factor, and process it.

[0049] In a possible design, the unmanned vehicle safety performance evaluation module is specifically: Obtain the unmanned vehicle driving safety factor of each monitoring sub-path in the corresponding driving path of the target unmanned vehicle, and compare it with the preset unmanned vehicle driving safety factor. If the unmanned vehicle driving safety factor of a certain monitoring sub-path is less than the preset unmanned vehicle driving safety factor, it indicates that the road driving of the unmanned vehicle on this sub-path does not meet the safety standards, and the current geographical location of the vehicle should be sent to the management personnel through the vehicle communication network. Otherwise, it indicates that the road driving of the unmanned vehicle on this sub-path meets the safety standards.

[0050] See also Figure 3 As shown, in this embodiment, it should be specifically explained that the present invention provides a human-computer interaction method for an unmanned vehicle, comprising the following steps: Step S01: unmanned vehicle driving path division: used to divide the driving path of the target unmanned vehicle into monitoring sub-paths of equal route length, and number each monitoring sub-path in the corresponding driving path of the target unmanned vehicle; Step S02: Unmanned vehicle driving road condition information collection: used to collect driving road condition data of each monitoring sub-path in the driving path corresponding to the target unmanned vehicle through a vehicle laser sensor, and the unmanned vehicle driving road condition information collection includes a vehicle driving power sensing unit and a vehicle driving road condition sensing unit; Step S03: unmanned vehicle driving power control: used to calculate the unmanned vehicle driving power control index of each monitoring sub-path in the driving path corresponding to the target unmanned vehicle according to the vehicle driving power sensing data of the vehicle driving power sensing unit, and convey the driving route of the vehicle with abnormal power; Step S04: unmanned vehicle road condition information analysis: used to calculate the unmanned vehicle driving performance index and the unmanned vehicle driving sensor perception index of each monitoring sub-path according to the vehicle driving road condition perception data of the vehicle driving road condition perception unit, and analyze and obtain the unmanned vehicle driving road condition information optimization index of each monitoring sub-path in the driving path corresponding to the target unmanned vehicle; Step S05: Unmanned vehicle safety performance: used to calculate the unmanned vehicle driving safety factor of each monitoring sub-path in the driving path corresponding to the target unmanned vehicle; Step S06: Unmanned vehicle safety performance evaluation: used to obtain the unmanned vehicle driving safety factor of each monitored sub-path in the corresponding driving path of the target unmanned vehicle, compare it with the preset unmanned vehicle driving safety factor, and process it.

[0051] In this embodiment, it should be specifically explained that the present invention collects the driving road condition data of each monitoring sub-path in the driving path corresponding to the target unmanned vehicle through the vehicle laser sensor, and calculates the unmanned vehicle driving power control index of each monitoring sub-path in the driving path corresponding to the target unmanned vehicle according to the vehicle driving power perception data of the vehicle driving power perception unit, and compares it with the preset unmanned vehicle driving power control index. If the unmanned vehicle driving power control index of a sub-path is greater than the preset unmanned vehicle driving power control index, it indicates that there is an abnormality in the vehicle power of the driving route of the unmanned vehicle, and the management personnel should be notified through the vehicle communication network to start the processing measures. Otherwise, it indicates that there is no abnormality in the vehicle power of the driving route. By continuously monitoring and analyzing the vehicle driving power data, the driving state of the unmanned vehicle can be evaluated in real time, reducing the need for manual intervention of the unmanned vehicle. At the same time, through the judgment of abnormalities, it is conducive to timely adjusting the driving state of the unmanned vehicle, and improving the convenience of human-computer interaction decision-making; The present invention collects the driving road condition data of each monitoring sub-path in the driving path corresponding to the target unmanned vehicle through a vehicle laser sensor, calculates the driving performance index of the unmanned vehicle and the driving sensor perception index of the unmanned vehicle in each monitoring sub-path according to the vehicle driving road condition perception data of the vehicle driving road condition perception unit, further analyzes and obtains the unmanned vehicle driving road condition information optimization index of each monitoring sub-path in the driving path corresponding to the target unmanned vehicle, thereby calculating the unmanned vehicle driving safety factor of each monitoring sub-path in the driving path corresponding to the target unmanned vehicle, and compares it with the preset unmanned vehicle driving safety factor. If the unmanned vehicle driving safety factor of a certain monitoring sub-path is less than the preset unmanned vehicle driving safety factor, it indicates that the road driving of the unmanned vehicle in the sub-path does not meet the safety standard, and the current geographical location of the vehicle should be sent to the management personnel through the vehicle communication network. Otherwise, it indicates that the road driving of the unmanned vehicle in the sub-path meets the safety standard. Through in-depth analysis of the data, the calculation accuracy of the driving safety of the unmanned vehicle on different routes is improved, which is conducive to comprehensive monitoring of the road driving safety of the unmanned vehicle. Through early warning of the driving safety of the unmanned vehicle, it is conducive to reducing the probability of unmanned vehicle accidents and improving the safety of the unmanned vehicle.

[0052] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A human-computer interaction system for an unmanned vehicle, characterized in that: include: Unmanned vehicle driving path division module: used to divide the driving path of the target unmanned vehicle into monitoring sub-paths of equal route length, and number each monitoring sub-path in the corresponding driving path of the target unmanned vehicle; The unmanned vehicle driving road condition information collection module is used to collect the driving road condition data of each monitoring sub-path in the driving path corresponding to the target unmanned vehicle through the vehicle laser sensor. The unmanned vehicle driving road condition information collection module includes a vehicle driving power sensing unit and a vehicle driving road condition sensing unit; Unmanned vehicle driving power control module: used to calculate the unmanned vehicle driving power control index of each monitoring sub-path in the driving path corresponding to the target unmanned vehicle according to the vehicle driving power sensing data of the vehicle driving power sensing unit, and convey the driving route of the vehicle with abnormal power; Unmanned vehicle road condition information analysis module: used to calculate the unmanned vehicle driving performance index and the unmanned vehicle driving sensor perception index of each monitoring sub-path according to the vehicle driving road condition perception data of the vehicle driving road condition perception unit, and analyze and obtain the unmanned vehicle driving road condition information optimization index of each monitoring sub-path in the driving path corresponding to the target unmanned vehicle; Unmanned vehicle safety performance module: used to calculate the unmanned vehicle driving safety factor of each monitoring sub-path in the driving path corresponding to the target unmanned vehicle; Unmanned vehicle safety performance evaluation module: used to obtain the unmanned vehicle driving safety factor of each monitoring sub-path in the corresponding driving path of the target unmanned vehicle, compare it with the preset unmanned vehicle driving safety factor, and process it.

2. The human-computer interaction system for an unmanned vehicle according to claim 1, characterized in that: The unmanned vehicle driving path division module is specifically: The driving path of the unmanned vehicle is determined through map information, and is divided into monitoring sub-paths according to the division method of equal route length, and the monitoring sub-paths in the driving path corresponding to the target unmanned vehicle are numbered 1, 2, ...i, ...n in sequence.

3. The human-computer interaction system for an unmanned vehicle according to claim 1, characterized in that: The unmanned vehicle driving road condition information collection module is specifically: Vehicle driving dynamics sensing unit: used to collect the wheel track, vehicle center of mass height, vehicle road slope, and vehicle air resistance of each monitoring sub-path in the corresponding driving path of the target unmanned vehicle through the vehicle laser sensor, which are marked as , , , , where i=1,2,...n, i represents the number of the i-th monitoring subpath; Vehicle road condition perception unit: used to collect the rolling resistance of the vehicle wheels, vehicle speed, vehicle sway angular velocity, vehicle light intensity, vehicle road surface humidity, and vehicle throttle opening of each monitoring sub-path in the corresponding driving path of the target unmanned vehicle through the vehicle laser sensor, which are marked as , , , , , .

4. The human-computer interaction system for an unmanned vehicle according to claim 1, characterized in that: The unmanned vehicle driving power control module is specifically: Step S41: in, It is represented as the driving dynamics control index of the unmanned vehicle in the ith monitoring subpath, It is represented as the vehicle air resistance of the i-th monitoring subpath, It is represented by the wheel track of the vehicle in the ith monitoring subpath, It is represented as the height of the vehicle center of mass of the ith monitoring subpath, It is represented as the road slope of the vehicle in the ith monitoring subpath; Step S42: Compare the unmanned vehicle driving power control index of each monitored sub-path in the corresponding driving path of the target unmanned vehicle with the preset unmanned vehicle driving power control index. If the unmanned vehicle driving power control index of a sub-path is greater than the preset unmanned vehicle driving power control index, it indicates that there is an abnormality in the vehicle power of the unmanned vehicle driving route, and the management personnel should be notified through the on-board communication network to initiate processing measures. Otherwise, it indicates that there is no abnormality in the vehicle power of the driving route.

5. The human-computer interaction system for an unmanned vehicle according to claim 1, characterized in that: The unmanned vehicle road condition information analysis module is specifically: Step S51: Calculate the unmanned vehicle driving performance index of each monitoring sub-path through the rolling resistance of the vehicle wheels, the vehicle driving speed, and the vehicle driving yaw angular velocity: in, It is represented as the driving performance index of the unmanned vehicle on the ith monitoring subpath, It is represented by the rolling resistance of the vehicle wheels in the i-th monitoring subpath, It is represented as the vehicle speed of the i-th monitoring sub-path, Indicates the preset vehicle speed. It is represented as the vehicle sway angular velocity of the i-th monitoring subpath, It is expressed as the vehicle sway angular velocity of the i-1th monitoring subpath, It is expressed as the maximum value of the vehicle's yaw angular velocity. It is expressed as the minimum value of the vehicle's sway angular velocity; Step S52: Calculate the sensor perception index of the unmanned vehicle driving on each monitoring sub-path according to the vehicle driving light intensity, vehicle driving road humidity, and vehicle driving throttle opening: in, It is represented as the sensor perception index of the unmanned vehicle driving on the i-th monitoring subpath, It is represented by the vehicle driving light intensity of the ith monitoring subpath, Represents the preset vehicle driving light intensity, It is represented by the road surface humidity of the vehicle in the ith monitoring subpath, Indicated as the preset vehicle driving road humidity, It is represented by the throttle opening fluctuation of the vehicle in the i-th monitoring subpath, It is represented by the throttle opening fluctuation of the vehicle in the i-1th monitoring subpath. It is represented as a natural constant, and n is the number of monitored subpaths; Step S53: The calculation formula of the optimization index of the road condition information of the unmanned vehicle is: in, It is represented as the optimization index of the road condition information of the unmanned vehicle on the i-th monitoring sub-path, It is expressed as the minimum value of the driving performance index of the unmanned vehicle. It is expressed as the maximum value of the driving performance index of the unmanned vehicle. It is expressed as the minimum value of the sensor perception index of the unmanned vehicle. It is expressed as the maximum value of the sensor perception index of the unmanned vehicle.

6. The human-computer interaction system for an unmanned vehicle according to claim 1, characterized in that: The unmanned vehicle safety performance module is specifically: Step S61: Obtain the wind speed of the target unmanned vehicle during driving on that day, marked as ; Step S62: The calculation formula of the driving safety factor of the unmanned vehicle is: in, It is represented as the driving safety factor of the unmanned vehicle in the ith monitoring subpath, Expressed as wind speed, It is represented as the driving dynamics control index of the unmanned vehicle in the ith monitoring subpath, It is represented as the vehicle speed of the i-th monitoring sub-path, Indicates the preset vehicle speed. It is represented as the throttle opening of the vehicle in the i-th monitoring subpath. It is represented as the optimization index of the road condition information of the unmanned vehicle on the i-th monitoring sub-path.

7. The human-computer interaction system for an unmanned vehicle according to claim 1, characterized in that: The unmanned vehicle safety performance evaluation module is specifically: Obtain the unmanned vehicle driving safety factor of each monitoring sub-path in the corresponding driving path of the target unmanned vehicle, and compare it with the preset unmanned vehicle driving safety factor. If the unmanned vehicle driving safety factor of a certain monitoring sub-path is less than the preset unmanned vehicle driving safety factor, it indicates that the road driving of the unmanned vehicle on this sub-path does not meet the safety standards, and the current geographical location of the vehicle should be sent to the management personnel through the vehicle communication network. Otherwise, it indicates that the road driving of the unmanned vehicle on this sub-path meets the safety standards.

8. A human-computer interaction method for an unmanned vehicle, using a human-computer interaction system for an unmanned vehicle as claimed in any one of claims 1 to 7, characterized in that: The following steps are involved: Step S01: unmanned vehicle driving path division: used to divide the driving path of the target unmanned vehicle into monitoring sub-paths of equal route length, and number each monitoring sub-path in the corresponding driving path of the target unmanned vehicle; Step S02: Unmanned vehicle driving road condition information collection: used to collect driving road condition data of each monitoring sub-path in the driving path corresponding to the target unmanned vehicle through a vehicle laser sensor, and the unmanned vehicle driving road condition information collection includes a vehicle driving power sensing unit and a vehicle driving road condition sensing unit; Step S03: unmanned vehicle driving power control: used to calculate the unmanned vehicle driving power control index of each monitoring sub-path in the driving path corresponding to the target unmanned vehicle according to the vehicle driving power sensing data of the vehicle driving power sensing unit, and convey the driving route of the vehicle with abnormal power; Step S04: unmanned vehicle road condition information analysis: used to calculate the unmanned vehicle driving performance index and the unmanned vehicle driving sensor perception index of each monitoring sub-path according to the vehicle driving road condition perception data of the vehicle driving road condition perception unit, and analyze and obtain the unmanned vehicle driving road condition information optimization index of each monitoring sub-path in the driving path corresponding to the target unmanned vehicle; Step S05: Unmanned vehicle safety performance: used to calculate the unmanned vehicle driving safety factor of each monitoring sub-path in the driving path corresponding to the target unmanned vehicle; Step S06: Unmanned vehicle safety performance evaluation: used to obtain the unmanned vehicle driving safety factor of each monitoring sub-path in the corresponding driving path of the target unmanned vehicle, compare it with the preset unmanned vehicle driving safety factor, and process it.

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