Intelligent sharing control system of intelligent unmanned scooter

Abstract

The invention provides an intelligent sharing control system of an intelligent unmanned scooter, and relates to the technical field of unmanned scooters, the intelligent sharing control system comprises an environment acquisition module, a positioning navigation module, a path generation module, a driving module, a communication module and a road surface type identification module, the communication module is in communication connection with a first terminal, a second terminal and a cloud computing system; the main control module is electrically connected with the environment acquisition module, the positioning navigation module, the path generation module, the driving module, the communication module, the road surface type identification module and the data processing system. The road surface type recognition module is arranged and can be used for recognizing the road surface on which the unmanned scooter runs, and work of the unmanned scooter can be controlled according to the recognition result of the road surface type recognition module.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned mobility scooters, and in particular to a smart shared control system for an intelligent unmanned mobility scooter. Background Art

[0002] A mobility scooter refers to a means of transportation and auxiliary tools for the purpose of walking. In China, it can sometimes refer to cars, electric bicycles, electric scooters, two-wheeled automatic balancing scooters, and unicycles. With the continuous development of unmanned driving technology, unmanned vehicles (unmanned scooters / unmanned scooters) are beginning to be used on urban standard roads and logistics roads. Existing unmanned scooters, such as the CN109204586A smart unmanned scooter, have the following problems: lack of recognition of road surface type, and inability to intelligently control the operation of the unmanned scooter according to the road surface type. Summary of the invention

[0003] The present invention is provided to solve the technical problems raised by the above background technology.

[0004] In order to solve the above technical problems, the present invention discloses a smart shared control system for an intelligent unmanned vehicle, comprising:

[0005] The environment acquisition module is used to collect obstacle information and terrain information around the unmanned vehicle;

[0006] A positioning and navigation module is used to obtain the current position of the unmanned vehicle. The positioning and navigation module is configured with a high-precision map;

[0007] A path generation module is used to generate a driving path for the unmanned vehicle based on a high-precision map, the current location of the unmanned vehicle, and the departure point and destination of the passenger;

[0008] A driving module, which is used to control the driving of the unmanned vehicle according to the driving path of the unmanned vehicle and the information collected by the environment collection module;

[0009] A communication module, the communication module is connected to the first terminal, the second terminal, and the cloud computing system for communication;

[0010] A road surface type recognition module, used to identify the road surface type of the road on which the unmanned mobility scooter is traveling;

[0011] The main control module is electrically connected to the environment acquisition module, the positioning and navigation module, the path generation module, the driving module, the communication module, and the road type identification module. The main control module is also electrically connected to the data processing system.

[0012] Preferably, the environment acquisition module includes a first camera module, a laser radar and an ultrasonic sensor;

[0013] The first camera module is used to obtain visual information of the environment where the unmanned vehicle is located and provide image and video data, which are used for map construction, motion control and navigation tasks;

[0014] LiDAR uses laser beams to measure the distance and position of obstacles around the self-driving car, generating accurate point cloud data, which can provide high-precision three-dimensional environmental perception, helping the self-driving car detect obstacles, build maps, measure distances and make obstacle avoidance decisions;

[0015] Ultrasonic sensors measure the distance to obstacles by sending and receiving ultrasonic signals. They are used for close-range obstacle detection and avoidance to avoid collisions with static obstacles.

[0016] Preferably, it also includes:

[0017] The air pressure measurement module is used to collect the atmospheric pressure value where the unmanned vehicle is located and convert it into the corresponding atmospheric pressure altitude value;

[0018] The positioning and navigation module is a global positioning system GPS module or a Beidou satellite navigation system, and also includes a near-orbit Starlink navigation system;

[0019] Pavement types include: pavement material type and pavement condition type. Pavement condition types include: flat pavement and bumpy pavement. Pavement material types specifically include: asphalt pavement, gravel pavement, dirt road and sand.

[0020] Preferably, it also includes:

[0021] The vehicle speed detection module is used to detect the current speed of the unmanned vehicle;

[0022] The speed limit information acquisition module is used to obtain the maximum permissible speed of the unmanned mobility scooter on the current road section;

[0023] The vehicle speed comparison module is used to compare the current driving speed of the unmanned vehicle with the maximum allowable driving speed of the current section of the road where the unmanned vehicle is located, and obtain the vehicle speed comparison result. The main control module controls the drive module to work according to the vehicle speed comparison result to control the driving speed of the unmanned vehicle to be no greater than the maximum allowable driving speed of the current section of the road where the unmanned vehicle is located.

[0024] Preferably, it also includes: an intelligent module for entertainment + office is set in the unmanned transportation vehicle, the intelligent module for entertainment + office includes: a vehicle-mounted laptop computer and a routing module, and the routing module is used to provide Internet access capability for the vehicle-mounted laptop computer.

[0025] Preferably, it also includes:

[0026] An order processing module, used for receiving a taxi order sent by the first terminal and dispatching the taxi order to the second terminal;

[0027] The vehicle location positioning module is used to locate the vehicle location based on the departure information in the taxi order information;

[0028] The drop-off point positioning module is used to locate the drop-off point based on the destination information in the taxi order information;

[0029] A fee generation module, used for generating and storing fee information according to the itinerary information between the passenger's departure place and the passenger's destination after the taxi order is completed at the second terminal;

[0030] The fee sending module sends the fee information to the first terminal and the second terminal when the taxi order is displayed as completed. The first terminal completes the payment for the ride through the mobility scooter platform.

[0031] Preferably, the data processing system is used to process the information collected by the environment collection module to determine the movement speed and position of the target moving obstacle in the driving environment of the unmanned scooter; the target moving obstacle is a moving obstacle with a risk of collision with the unmanned scooter in the moving direction, specifically a moving obstacle close to the unmanned scooter along the width direction of the unmanned scooter; the intelligent shared control system of the intelligent unmanned scooter also includes:

[0032] The third storage module stores the position and size information of different functional areas around the unmanned scooter. The collection range of the environment collection module includes all functional areas, including the first-level deceleration area, the second-level deceleration area, the third-level deceleration area, and the stop area. Along the length direction of the unmanned scooter, the distance range between different functional areas and the front end of the unmanned scooter is different;

[0033] The acquisition module is used to obtain the operation information of the unmanned vehicle, and the operation information of the unmanned vehicle includes: the operation speed of the unmanned vehicle;

[0034] The fourth calculation module is used to calculate the minimum target distance between each functional area and the front end of the unmanned mobility scooter; when it is detected that the target moving obstacle enters the corresponding functional area, the main control module controls the driving module to execute the function of the corresponding functional area;

[0035]

[0036] B z B is the minimum target distance between the zth functional area and the front end of the unmanned vehicle along the length direction of the unmanned vehicle; z0 is the preset minimum distance between the zth functional area and the front end of the unmanned scooter along the length direction of the unmanned scooter; lg is the logarithm with base 10; min represents the minimum value; μ is the friction coefficient of the unmanned scooter on the current type of road surface; δ is the slip rate of the unmanned scooter on the current type of road surface; is the current speed of the autonomous vehicle; θty The shortest distance of the t-th target moving obstacle in the collection range of the environment collection module from the unmanned vehicle along the direction parallel to the width of the unmanned vehicle; is the tth target moving obstacle in the collection range of the environment collection module along θ ty The speed of movement in the direction The direction of movement is the positive direction; t dz The time required for the current unmanned vehicle to complete the function corresponding to the zth functional area; t 0 The reaction time of the autonomous vehicle to start executing the function corresponding to the zth functional area; for The corresponding distance adjustment coefficient; For combination The obtained distance adjustment coefficient.

[0037] Preferably, an automatic weighing module and a display module are arranged inside the body of the unmanned mobility scooter, and the automatic weighing module and the display module are electrically connected to the main control module respectively.

[0038] Preferably, it also includes a battery power management module, which includes a battery pack, a charging module, a charging and discharging chip and a power detection chip; the power detection chip includes a CPU, a voltage monitoring unit, a current monitoring unit, a battery status detection unit and a bus communication unit.

[0039] Preferably, it also includes:

[0040] The third acquisition module obtains a power change curve of the remaining power of the unmanned vehicle after the last E times of charging as a function of usage time;

[0041] A screening and marking unit is used to obtain the sub-curve segment where the unmanned vehicle is traveling in the power change curve, and mark the corresponding road segment type in the sub-curve segment corresponding to different types of road segments, and the road segment types include: flat road segment type, first-level bumpy road segment type, second-level bumpy road segment type, and third-level bumpy road segment type;

[0042] The first storage module stores the travel speed of the unmanned vehicle passing through the bumpy road section within a preset time period and the bumpiness level of the bumpy road section;

[0043] The transport task acquisition module is used to acquire the information of the current transport task, and the information of the current transport task includes: the type and length of the road section in the driving path of the unmanned vehicle executing the current transport task;

[0044] A second calculation module is used to calculate a first availability of each unmanned mobility scooter that is currently idle and can perform a current transportation task;

[0045]

[0046] S r1 is the first availability of the rth unmanned mobility vehicle that is currently idle and can perform the current transportation task; H is the total number of road segment types on the driving path of the unmanned mobility vehicle that performs the current transportation task; L h is the length of the hth type of road section in the driving path of the unmanned vehicle performing the current transportation task; v hr The average driving speed of the rth unmanned mobility vehicle that is currently idle and can perform the current transportation task and passes through the hth type of road section obtained based on the first storage module; Y r The total number of sub-curve segments of the h-th road segment type selected for the calculation of the r-th autonomous vehicle that is currently idle and can perform the current transportation task; G rhy The amount of power reduction of the selected sub-curve segment of the yth hth road segment type for the calculation of the rth unmanned mobility vehicle that is currently idle and can perform the current transportation task; t rhy The driving time of the selected sub-curve segment of the yth hth road segment type for the calculation of the rth unmanned mobility vehicle that is currently idle and can perform the current transportation task; K rhy1 K is the average value of the absolute value of the inflection point of the yth sub-curve segment of the hth road segment type selected for the calculation of the rth unmanned mobility vehicle that is currently idle and can perform the current transportation task; rhy2 The maximum absolute value of the slope of the inflection point of the yth sub-curve segment of the hth road segment type selected for the calculation of the rth unmanned mobility vehicle that is currently idle and can perform the current transportation task; G r is the remaining power of the rth unmanned mobility vehicle that is currently idle and can perform the current transportation task; max represents the maximum value; ln is the natural logarithm, and e is a natural constant;

[0047] The primary screening module is used to select an unmanned vehicle that is currently idle and can perform the current transportation task and has an availability greater than a preset availability as the first unmanned vehicle.

[0048] Preferably, it also includes:

[0049] The second storage module further stores vibration information detected by a second vibration sensor of the first unmanned vehicle passing through a bumpy road section within a preset time period, and the second vibration sensor is arranged in the cargo compartment of the first unmanned vehicle;

[0050] A third calculation module, configured to calculate a second availability of each first unmanned mobility vehicle;

[0051]

[0052]

[0053] S r2 is the second availability of the rth first unmanned vehicle; x vur1(t) The vibration acceleration signal obtained by the second vibration sensor when the rth first unmanned mobility vehicle passes the bumpy road section of the vth level for the uth time and stored by the second storage module; T 2 is the sampling time of the vibration acceleration signal corresponding to the second vibration sensor; The vibration frequency of the rth first unmanned mobility vehicle obtained by the second vibration sensor when the rth first unmanned mobility vehicle passes through the bumpy road section of the vth level for the uth time stored by the second storage module; vur1 is the maximum amplitude obtained by the second vibration sensor when the rth first unmanned mobility vehicle passes the bumpy road section of the vth level for the uth time stored in the second storage module; F vur is the equivalent vibration parameter obtained by the second vibration sensor based on the second storage module when the rth first unmanned mobility vehicle passes through the bumpy road section of the vth level for the uth time; g is the gravitational acceleration; ∝ 1 ,∝ 2 They are the first vibration evaluation coefficient and the second vibration evaluation coefficient respectively; 1 is the maximum permissible equivalent vibration parameter for the current transportation task; 1v is the preset reference vibration evaluation value of the bumpy road section of level v; rv The number of bumpy sections of the vth level determined by the rth first unmanned vehicle based on the information stored in the second storage module Greater than P 1v The number of times; P 1v For the bumpy road section of level v Corresponding preset reference value; H 1rv is the total number of times the rth first unmanned mobility vehicle passes through the vth level bumpy road section determined based on the information stored in the second storage module; R rv is the vibration evaluation value of the rth first unmanned mobility vehicle passing through the vth level bumpy road section; max represents the maximum value;

[0054] a third calculation module, configured to calculate the comprehensive availability of each first unmanned mobility scooter based on the second calculation module and the third calculation module, select a first unmanned mobility scooter whose comprehensive availability is greater than a preset comprehensive availability as a target first unmanned mobility scooter, and use the target first unmanned mobility scooter to perform the current transportation task;

[0055] S r =∈ 1 S r1 +∈ 2 S r2 ;

[0056] S ris the comprehensive availability of the rth first unmanned vehicle, ∈ 1 ,∈ 2 They are the first availability weight and the second availability weight respectively.

[0057] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] The unmanned vehicle of the present invention can be used to carry people and / or goods;

[0060] The environment acquisition module collects obstacle information and terrain information about the environment around the unmanned vehicle; the positioning and navigation module obtains the current position of the unmanned vehicle, and the positioning and navigation module is equipped with a high-precision map; the path generation module generates the driving path of the unmanned vehicle based on the high-precision map, the current position of the unmanned vehicle, and the departure point and destination of the passengers; the driving module controls the driving of the unmanned vehicle based on the driving path of the unmanned vehicle and the information collected by the environment acquisition module, so as to enable the unmanned vehicle to carry people and / or goods to the destination. The communication module is used to communicate with the cloud computing system, the shipping terminal, and the receiving terminal and feedback data;

[0061] The main control module controls the operation of the environment acquisition module, the positioning and navigation module, the path generation module, the driving module, the communication module, and the road surface type recognition module and obtains the information collected by the environment acquisition module, the positioning and navigation module, the path generation module, the driving module, the communication module, and the road surface type recognition module. The main control module performs data processing based on the data processing system.

[0062] The present invention provides a road type recognition module, which can be used to identify the road surface on which the unmanned mobility scooter travels, and can control the operation of the unmanned mobility scooter according to the recognition result of the road type recognition module (such as controlling the driving module to adjust the driving speed of the unmanned mobility scooter; or according to the reduction in driving power corresponding to different road types and the different vibration states of the unmanned mobility scooter, and selecting a suitable unmanned mobility scooter for transportation according to the road surface type of the unmanned mobility scooter's driving path based on information about the transportation task; or adjusting the distance between the following functional area and the front end of the unmanned mobility scooter). BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0064] Figure 1 is a schematic diagram of a system of the present invention;

[0065] Figure 2This is an optional appearance diagram of the unmanned vehicle of the present invention. DETAILED DESCRIPTION

[0066] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0067] In addition, in the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes, and do not specifically refer to the order or sequence, nor are they used to limit the present invention. They are only used to distinguish components or operations described with the same technical terms, and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions and technical features between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0068] The present invention provides the following embodiments

[0069] Embodiment 1, the embodiment of the present invention provides, as Figure 1-Figure 2 As shown, including:

[0070] The environment acquisition module is used to collect obstacle information and terrain information around the unmanned vehicle;

[0071] A positioning and navigation module is used to obtain the current position of the unmanned vehicle. The positioning and navigation module is configured with a high-precision map;

[0072] A path generation module is used to generate a driving path for the unmanned vehicle based on a high-precision map, the current location of the unmanned vehicle, and the departure point and destination of the passenger;

[0073] A driving module, which is used to control the driving of the unmanned vehicle according to the driving path of the unmanned vehicle and the information collected by the environment collection module;

[0074] The communication module is connected to the first terminal, the second terminal and the cloud computing system in communication; the present invention can realize data sharing among the first terminal, the second terminal and the cloud computing system.

[0075] A road surface type recognition module, used to identify the road surface type of the road on which the unmanned mobility scooter is traveling;

[0076] The main control module is electrically connected to the environment acquisition module, the positioning and navigation module, the path generation module, the driving module, the communication module, and the road type identification module. The main control module is also electrically connected to the data processing system.

[0077] Preferably, it also includes an intelligent recognition module, which is electrically connected to the main control module. The intelligent recognition module includes any one or more of an ID card recognition module, a face recognition module, a fingerprint recognition module, a telephone number recognition module and a voice interaction module. An intelligent recognition module is provided on the vehicle body, which can realize automatic intelligent authentication of users who make appointments or remote summons. For those who have not obtained background authorization to use the vehicle, the vehicle does not provide door opening and automatic driving functions. After the user gets off at the predetermined destination, the scooter will automatically return to the designated parking location. When the unmanned scooter is parked and waiting for customers to use it, the vehicle's automatic driving system is in a dormant or closed state; a camera and a positioning module are also provided on the vehicle body, and a portrait recognition module is provided in the camera. The vehicle body is provided with an anti-theft lock and an alarm device. If the anti-theft lock is opened abnormally, the alarm device is triggered, and the portrait recognition module is triggered to automatically record and retain evidence.

[0078] Preferably, the road surface type includes: road surface material type and road surface state type. The road surface state type includes: flat road surface and bumpy road surface. The road surface material type specifically includes: asphalt road surface, gravel road surface, dirt road and sand.

[0079] Among them, the first terminal is the shipping terminal; the second terminal is the receiving terminal, and the communication module is used to exchange messages and feedback data with the cloud computing system, the main control module, the shipping terminal and the receiving terminal.

[0080] The positioning and navigation module is a global positioning system GPS module or a Beidou satellite navigation system.

[0081] The environment acquisition module can sense static obstacles and moving obstacles. Static obstacles are sensed through the first camera module (including multiple cameras at different positions, used to obtain video images of the unmanned vehicle in different directions), lidar and ultrasonic sensors. Cameras are one of the most common perception sensors. They help unmanned vehicles obtain visual information about the environment and can provide image and video data. The data can be used for map construction, motion control and navigation tasks. Lidar uses laser beams to measure the distance and position of surrounding obstacles and generate accurate point cloud data. It can provide high-precision three-dimensional environmental perception and help unmanned vehicles detect obstacles, build maps, measure distances and make obstacle avoidance decisions. Ultrasonic sensors measure the distance of obstacles by sending and receiving ultrasonic signals. They are used for close-range obstacle detection and avoidance to avoid collisions with static obstacles. Dynamic obstacles are sensed through a variety of sensors, including: infrared sensors, which can detect the heat radiation of surrounding obstacles and are used to detect human bodies, animals and other heat sources. This is very useful for target detection and obstacle avoidance in low-light conditions or in areas with dense vegetation; radar sensors, which use electromagnetic waves to detect the position and movement of obstacles. It can provide longer-range detection capabilities and is very effective for target detection and obstacle avoidance in complex weather conditions or in environments with limited visible light.

[0082] The environment acquisition module may also adopt other existing devices for detecting terrain and obstacles.

[0083] The road surface type recognition module includes:

[0084] The second camera is used to obtain images of the road on which the unmanned vehicle is traveling; the first image processing module preprocesses the images of the road on which the unmanned vehicle is traveling, classifies and identifies the preprocessed images of the road on which the unmanned vehicle is traveling (which can be identified based on a preset road type recognition model), and determines the classification result of the current image of the road on which the unmanned vehicle is traveling.

[0085] The intelligent shared control system of the intelligent unmanned scooter also includes: a lane recognition module, which is used to identify the lane where the unmanned scooter is located, and the lane recognition module includes a second camera, which is used to obtain the image of the road where the unmanned scooter is traveling; a second image processing module, which pre-processes the image of the road where the unmanned scooter is traveling obtained by the camera to determine the lane line, and judges the lane where the unmanned scooter is located according to the lane line;

[0086] The ultrasonic sensor is used to obtain the depth / elevation between the road surface and the location where the ultrasonic sensor is installed (such as the wheel), and to determine whether the road surface is flat based on the elevation change.

[0087] The road surface type identification module may also adopt an existing device for road surface type identification.

[0088] The present invention also includes a traffic sign recognition module, which includes a video recognition device for shooting the road scene in front of the vehicle, distinguishing whether there are traffic lights and zebra crossings in front, and identifying the state of the traffic lights, and at the same time calculating the real-time spatial distance between the vehicle and the zebra crossing according to the pixel value of the zebra crossing in the video picture, and sending the continuously shortened spatial distance value between the two to the data processing module; the data processing module combines the state information of the traffic light in front, and compares the continuously shortened spatial distance value between the vehicle and the zebra crossing according to the preset red light running distance critical value, to determine whether the vehicle will run a red light; the video recognition device includes a video acquisition module and a video recognition module, the video acquisition module is used to shoot the road scene in front of the vehicle, and the video recognition module is used to analyze and calculate the acquired picture; the video recognition module includes a traffic light recognition module and a zebra crossing recognition module, the traffic light recognition module is used to calculate the state of the traffic light in the acquired picture; the zebra crossing recognition module is used to analyze and calculate the spatial distance between the zebra crossing in the acquired picture and the vehicle. The data processing module is also connected to the ECU. If the data processing module determines that the vehicle will run a red light, the ECU controls the vehicle. The data processing module is also connected to the cloud computing system. The data processing module can send the judgment result of whether the vehicle will run a red light to the cloud computing system. The cloud computing system can feedback command instructions to the data processing module, and the vehicle is controlled by connecting the data processing module to the ECU.

[0089] It also includes an obstacle avoidance module; the obstacle avoidance module includes an obstacle information storage device for storing the orientation and distance properties of detected obstacles, an obstacle avoidance operation code storage device for storing obstacle avoidance operation codes, and a comparator for comparing the detected obstacle information storage device with the obstacle avoidance operation code storage device to replace the control signal input device to instruct the vehicle to avoid obstacles; for details, please refer to CN109204586A intelligent unmanned walking vehicle;

[0090] The unmanned vehicle for mobility commuting of the present invention can be used as a household vehicle for mobility commuting and a commercial vehicle for mobility commuting.

[0091] Also includes:

[0092] The air pressure measurement module is used to collect the atmospheric pressure value where the unmanned vehicle is located and convert it into the corresponding atmospheric pressure altitude value; by obtaining the altitude information of the unmanned vehicle, it is convenient to obtain the location of the unmanned vehicle;

[0093] The positioning and navigation module is a global positioning system GPS module or a Beidou satellite navigation system;

[0094] Pavement types include: pavement material type and pavement condition type. Pavement condition types include: flat pavement and bumpy pavement. Pavement material types specifically include: asphalt pavement, gravel pavement, dirt road and sand.

[0095] Preferably, it also includes:

[0096] The vehicle speed detection module is used to detect the current speed of the unmanned vehicle;

[0097] The speed limit information acquisition module is used to obtain the maximum permissible speed of the unmanned mobility scooter on the current road section;

[0098] The speed comparison module is used to compare the current speed of the unmanned scooter with the maximum allowable speed of the current section of the road where the unmanned scooter is located, and obtain the speed comparison result. The main control module controls the driving module to work according to the speed comparison result to control the speed of the unmanned scooter to be no greater than the maximum allowable speed of the current section of the road where the unmanned scooter is located. Through speed limitation, safe operation is achieved in a comfortable condition.

[0099] Preferably, the method further includes: setting an intelligent module for entertainment and office in the unmanned vehicle, the intelligent module for entertainment and office includes: a vehicle-mounted laptop and a routing module, the routing module is used to provide Internet access for the vehicle-mounted laptop, so that the user can play games, watch TV or work in the vehicle.

[0100] Preferably, it also includes:

[0101] An order processing module, used for receiving a taxi order sent by the first terminal and dispatching the taxi order to the second terminal;

[0102] The vehicle location positioning module is used to locate the vehicle location based on the departure information in the taxi order information;

[0103] The drop-off point positioning module is used to locate the drop-off point based on the destination information in the taxi order information;

[0104] A fee generation module, used for generating and storing fee information according to the itinerary information between the passenger's departure place and the passenger's destination after the taxi order is completed at the second terminal;

[0105] The fee sending module sends the fee information to the first terminal and the second terminal when the taxi order is displayed as completed. The first terminal completes the payment for the ride through the mobility scooter platform.

[0106] The cloud computing system is used to coordinate the allocation and management of the unmanned mobility scooters and to feed back relevant information to the unmanned mobility scooters and terminal devices respectively;

[0107] When the present invention is operated in the market, users can implement the function by following the steps below:

[0108] 1. Users download and install the APP on their mobile phones and register to become users of the mobility scooter platform. Through the APP, they can search for nearby vehicles and make reservations across time periods, or make instant calls. When making reservations or calls, users need to enter the designated boarding location and the passenger's departure and destination on the APP. According to user needs, the call can be accurately directed to a building in a certain community.

[0109] 2. When the user makes an appointment or calls a vehicle, the mobility scooter backend system automatically matches the nearest vehicle for the user to use based on the user's starting location, and the mobility scooter will automatically drive from the parking position to the boarding position.

[0110] 3. After using the vehicle, the user can pay the fare directly from the APP or by bank card, or by coin. The unmanned vehicles are used for short-distance passenger transportation in the city, so the settlement method is flexible and diverse. It can be subscribed to a monthly membership, or it can be charged by the number of times, by distance, or by time.

[0111] The working principle and beneficial effects of the above technical solution are:

[0112] The unmanned mobility scooter of the present invention can be used to carry people and / or goods; the present invention can be applied to non-motorized vehicle lanes, where the speed of vehicles is relatively slow and the road conditions are relatively simple. The present invention can choose to travel on non-motorized vehicle lanes in terms of path planning, and its travel speed will meet the requirements of traffic regulations.

[0113] The environment acquisition module collects obstacle information and terrain information about the environment around the unmanned vehicle; the positioning and navigation module obtains the current position of the unmanned vehicle, and the positioning and navigation module is equipped with a high-precision map; the path generation module generates the driving path of the unmanned vehicle based on the high-precision map, the current position of the unmanned vehicle, and the departure point and destination of the passengers; the driving module controls the driving of the unmanned vehicle based on the driving path of the unmanned vehicle and the information collected by the environment acquisition module, so as to enable the unmanned vehicle to carry people and / or goods to the destination. The communication module is used to communicate with the cloud computing system, the shipping terminal, and the receiving terminal and feedback data;

[0114] The main control module controls the operation of the environment acquisition module, the positioning and navigation module, the path generation module, the driving module, the communication module, and the road surface type recognition module and obtains the information collected by the environment acquisition module, the positioning and navigation module, the path generation module, the driving module, the communication module, and the road surface type recognition module. The main control module performs data processing based on the data processing system.

[0115] The present invention provides a road type recognition module, which can be used to identify the road surface on which the unmanned mobility scooter travels, and can control the operation of the unmanned mobility scooter according to the recognition result of the road type recognition module (such as controlling the driving module to adjust the driving speed of the unmanned mobility scooter; or according to the reduction in driving power corresponding to different road types and the different vibration states of the unmanned mobility scooter, and selecting a suitable unmanned mobility scooter for transportation according to the road surface type of the unmanned mobility scooter's driving path based on information about the transportation task; or adjusting the distance between the following functional area and the front end of the unmanned mobility scooter).

[0116] Embodiment 2, based on embodiment 1, further comprises:

[0117] A first acquisition module is used to acquire first information about a bumpy road section that the unmanned mobility scooter passes through from a departure point to a destination, wherein the first information about the bumpy road section includes: a road surface material type of the bumpy road section, a length of the bumpy road section, and a bumpiness level of the bumpy road section;

[0118] A second acquisition module is used to obtain second information of the test unmanned scooter passing through a bumpy road section, the second information including: vehicle speed, vibration information of the test unmanned scooter detected by a first vibration sensor, and information detected by a first distance sensor; the test unmanned scooter is provided with a first distance sensor, and the first distance sensor is used to detect the height of a key area of ​​the vehicle chassis from the ground; for a fixed cargo delivery route and the cargo being delivered is easily damaged, the test unmanned scooter is used to perform a walking test through a bumpy road section (the test unmanned scooter for a fixed cargo delivery route is periodically tested through a bumpy road section to obtain the actual vibration condition of the current bumpy road section based on the unmanned scooter);

[0119] A curve construction module, used to construct a height change curve with the detection time as the horizontal coordinate and the detection value of the first distance sensor as the vertical coordinate;

[0120] The first calculation module is used to calculate the target clamping force F of the clamping device in the cargo cavity of the unmanned mobility scooter on the current cargo based on the first acquisition module, the second acquisition module, and the curve construction module; the actual clamping force of the clamping device in the cargo cavity of the unmanned mobility scooter on the current cargo for a fixed cargo transportation route and for transporting the current cargo is the target clamping force;

[0121]

[0122] n is the total number of bumpy sections that the unmanned mobility scooter passes through from the departure point to the destination on the fixed cargo delivery route; a i , b i , c iThe road material type number, length, and bumpiness level of the ith bumpy road section that the unmanned mobility vehicle passes through on the fixed cargo delivery route from the departure point to the destination (the road parameters can be collected in advance and judged according to preset rules, such as: the depth of the road ditch slope, the number of ditch slopes of a certain length of road surface, the distance between ditch slopes, etc.; it can also be obtained based on the existing road ditch level judgment method, such as CN117125075A, CN111708372A; a i , b i , c i is obtained based on recent pavement assessment data); f(a i , b i , c i ) is combined with a i , b i , c i Obtain the clamping force adjustment coefficient of the i-th bumpy road section that the unmanned vehicle passes through on the fixed cargo delivery route from the departure point to the destination (obtained based on a preset bumpy road section pavement material type number-length-bumping grade mapping table); F 0 is the theoretical clamping force of the clamping device in the cargo cavity of the unmanned vehicle on the current cargo when the unmanned vehicle is running smoothly; x 1i(t) is the vibration acceleration signal obtained by the first vibration sensor during the i-th bumpy road section passed by the unmanned vehicle from the departure point to the destination of the fixed cargo delivery route; T is the sampling time of the vibration acceleration signal; C i1 The maximum amplitude obtained by the first vibration sensor during the i-th bumpy road section passed by the unmanned mobility scooter from the departure point to the destination of the fixed cargo delivery route; is the vibration frequency obtained by the first vibration sensor during the i-th bumpy road section passed by the unmanned vehicle from the departure point to the destination of the fixed cargo delivery route; g is the gravitational acceleration; ∝ 1 ,∝ 2 are the first vibration evaluation coefficient and the second vibration evaluation coefficient (both are greater than 0 and less than 1); M i H is the total number of inflection points of the height variation curve corresponding to the first distance sensor obtained during the i-th bumpy road section passed by the unmanned vehicle on the fixed cargo delivery route from the departure point to the destination; ijk is the absolute value of the slope of the kth inflection point of the height change curve corresponding to the first distance sensor obtained during the i-th bumpy road section passed by the unmanned mobility vehicle from the departure point to the destination of the fixed cargo delivery route; M i1 M is the number of times the absolute value of the difference between the two detections of the first distance sensor is greater than the preset difference during the i-th bumpy road section passed by the unmanned vehicle on the fixed cargo delivery route from the departure point to the destination; i2is the total number of detections by the first distance sensor during the i-th bumpy road section passed by the unmanned vehicle from the departure point to the destination on the fixed cargo delivery route; σ 1 , σ 2 are the first evaluation weight and the second evaluation weight respectively (both are greater than 0 and less than 1); τ 1 , τ 2 are the first adjustment coefficient evaluation weight and the second adjustment coefficient evaluation weight (both values ​​are greater than 0 and less than 1); For combination The clamping force adjustment coefficient obtained (based on the preset -Clamping force adjustment coefficient mapping table is obtained); ln is the natural logarithm, and e is the natural constant.

[0123] The number of the first distance sensors may be one or more. When there are more than one first distance sensors, Taking an average of the values ​​calculated by the formula by the multiple first distance sensors;

[0124] The beneficial effects of the above technical solution are:

[0125] This embodiment is suitable for determining the target clamping force of the current cargo by the clamping device in the cargo cavity of the unmanned mobility scooter during a fixed cargo delivery route with multiple deliveries and in which the transported cargo is easily damaged; the actual clamping force of the current cargo by the clamping device in the cargo cavity of the unmanned mobility scooter for the fixed cargo delivery route and the unmanned mobility scooter for transporting the current cargo is the target clamping force; thereby ensuring reliable clamping of the current cargo when transporting the fixed cargo delivery route to avoid damage to the current cargo.

[0126] It reflects the clamping force adjustment coefficient determined based on historical experience taking into account the road material type, length, and bump level; In order to combine the vibration conditions of the current actual bumpy road section The clamping force adjustment factor is determined based on the actual elevation fluctuation of the current bumpy road section. Corrections are made to make the calculation more reliable.

[0127] Embodiment 3, based on Embodiment 1 or 2, further comprises:

[0128] The third acquisition module obtains a power change curve of the remaining power of the unmanned vehicle after the last E times of charging as a function of usage time;

[0129] The screening and marking unit is used to obtain the sub-curve segment of the unmanned vehicle in the power change curve, and mark the corresponding road segment type in the sub-curve segment corresponding to the different types of road segments. The road segment types include: flat road segment type, first-level bumpy road segment type, second-level bumpy road segment type, and third-level bumpy road segment type (the more severe the bump, the higher the level). A first vibration sensor can also be set on the unmanned vehicle to The larger it is, the higher the level of turbulence);

[0130] The first storage module stores the travel speed of the unmanned vehicle passing through the bumpy road section within a recent preset time period and the bumpiness level of the bumpy road section;

[0131] The transport task acquisition module is used to acquire the information of the current transport task, and the information of the current transport task includes: the type and length of the road section in the driving path of the unmanned vehicle executing the current transport task;

[0132] A second calculation module, used to calculate a first availability of each unmanned mobility scooter that is currently idle and can perform a current transportation task;

[0133]

[0134] S r1 is the first availability of the rth unmanned mobility vehicle that is currently idle and can perform the current transportation task; H is the total number of road segment types on the driving path of the unmanned mobility vehicle that performs the current transportation task; L h is the length of the hth type of road section in the driving path of the unmanned vehicle performing the current transportation task; v hr The average driving speed of the rth unmanned mobility vehicle that is currently idle and can perform the current transportation task and passes through the hth type of road section obtained based on the first storage module; Y r The total number of sub-curve segments of the h-th road segment type selected for the calculation of the r-th autonomous vehicle that is currently idle and can perform the current transportation task; G rhy The amount of power reduction of the selected sub-curve segment of the yth hth road segment type for the calculation of the rth unmanned mobility vehicle that is currently idle and can perform the current transportation task; t rhy The driving time of the selected sub-curve segment of the yth hth road segment type for the calculation of the rth unmanned mobility vehicle that is currently idle and can perform the current transportation task; K rhy1 K is the average value of the absolute value of the inflection point of the yth sub-curve segment of the hth road segment type selected for the calculation of the rth unmanned mobility vehicle that is currently idle and can perform the current transportation task; rhy2 The maximum absolute value of the slope of the inflection point of the yth sub-curve segment of the hth road segment type selected for the calculation of the rth unmanned mobility vehicle that is currently idle and can perform the current transportation task; G ris the remaining power of the rth unmanned mobility vehicle that is currently idle and can perform the current transportation task; max represents the maximum value; ln is the natural logarithm, and e is a natural constant;

[0135] The primary screening module is used to select an unmanned vehicle that is currently idle and can perform the current transportation task and has an availability greater than a preset availability as the first unmanned vehicle.

[0136] The beneficial effects of the above technical solution are:

[0137] Obtain the power change curve of the remaining power of the unmanned vehicle after the last E times of charging as a function of usage time, and select the sub-curve segments corresponding to different types of road sections and the road section types corresponding to the marks, to obtain the recent power consumption status of different unmanned vehicles on different road section types;

[0138] The first storage module stores the travel speed of the unmanned vehicle passing through the bumpy road section within a preset time period and the bumpiness level of the bumpy road section, and obtains the travel status of different unmanned vehicles passing through the bumpy road section recently;

[0139] The transport task acquisition module is used to acquire the information of the current transport task, and the information of the current transport task includes: the type and length of the road section in the driving path of the unmanned vehicle executing the current transport task;

[0140] Based on the information of the current transport task, the recent power consumption status of different unmanned vehicles on different road sections, and the recent travel status of different unmanned vehicles on bumpy roads, the first availability (remaining power G) of each unmanned vehicle that is currently idle and can perform the current transport task is obtained. r The amount of electricity required to perform the current transportation task The ratio state), the unmanned vehicle that is currently idle and can perform the current transportation task with an availability greater than the preset availability is the first unmanned vehicle, and the first unmanned vehicle performs the current transportation task to ensure the power reliability of the execution of the current transportation task.

[0141] Embodiment 4, based on embodiment 3, further comprises:

[0142] The second storage module further stores vibration information detected by a second vibration sensor of the first unmanned vehicle passing through a bumpy road section within a preset time period, and the second vibration sensor is arranged in the cargo compartment of the first unmanned vehicle;

[0143] A third calculation module, configured to calculate a second availability of each first unmanned mobility vehicle;

[0144]

[0145] S r2is the second availability of the rth first unmanned vehicle; x vur1(t) The vibration acceleration signal obtained by the second vibration sensor when the rth first unmanned mobility vehicle passes the bumpy road section of the vth level for the uth time and stored by the second storage module; T 2 is the sampling time of the vibration acceleration signal corresponding to the second vibration sensor; The vibration frequency of the rth first unmanned mobility vehicle obtained by the second vibration sensor when the rth first unmanned mobility vehicle passes through the bumpy road section of the vth level for the uth time stored by the second storage module; vur1 is the maximum amplitude obtained by the second vibration sensor when the rth first unmanned mobility vehicle passes the bumpy road section of the vth level for the uth time stored in the second storage module; F vur is the equivalent vibration parameter acquired by the second vibration sensor based on the second storage module when the rth first unmanned mobility vehicle passes through the bumpy road section of the vth level for the uth time; g is the gravitational acceleration; o 1 is the maximum permissible equivalent vibration parameter for the current transportation task; 1v is the preset reference vibration evaluation value of the bumpy road section of level v; rv The number of bumpy sections of the vth level determined by the rth first unmanned vehicle based on the information stored in the second storage module Greater than P 1v The number of times; P 1v For the bumpy road section of level v Corresponding preset reference value; H 1rv is the total number of times the rth first unmanned mobility vehicle passes through the vth level bumpy road section determined based on the information stored in the second storage module; R rv is the vibration evaluation value of the rth first unmanned mobility vehicle passing through the vth level bumpy road section; max represents the maximum value;

[0146] a third calculation module, configured to calculate the comprehensive availability of each first unmanned mobility scooter based on the second calculation module and the third calculation module, select a first unmanned mobility scooter whose comprehensive availability is greater than a preset comprehensive availability as a target first unmanned mobility scooter, and use the target first unmanned mobility scooter to perform the current transportation task;

[0147] S r =∈ 1 S r1 +∈ 2 S r2 ;

[0148] S r is the comprehensive availability of the rth unmanned vehicle, ∈ 1 ,∈ 2 They are the first availability weight and the second availability weight respectively (both are greater than 0 and less than 1).

[0149] The beneficial effects of the above technical solution are:

[0150] Different first unmanned vehicles have different vibration states when passing through bumpy sections due to different long-term use states. Based on the second storage module, the vibration state of the first unmanned vehicle passing through the bumpy section in the recent period is obtained to obtain the equivalent vibration parameter R of the first unmanned vehicle passing through each level of bumpy section. rv , and determining the second availability (the degree to which the vibration state satisfies the transportation demand) of each first unmanned vehicle based on the equivalent vibration parameters of the first unmanned vehicle passing through each level of bumpy road section and the maximum allowable equivalent vibration parameters of the current transportation task;

[0151] Finally, the comprehensive availability of each first unmanned vehicle is obtained based on the first availability and the second availability, and the first unmanned vehicle with a comprehensive availability greater than the preset comprehensive availability is selected as the target first unmanned vehicle. The target first unmanned vehicle performs the current transportation task to ensure the reliable execution of the current transportation task.

[0152] Embodiment 5, based on any one of Embodiments 1-4, the data processing system is used to process the information collected by the environment collection module to determine the movement speed and position of the target moving obstacle in the driving environment of the unmanned scooter; the target moving obstacle is a moving obstacle with a risk of collision with the unmanned scooter in the moving direction, specifically a moving obstacle close to the unmanned scooter along the width direction of the unmanned scooter; the intelligent shared control system of the intelligent unmanned scooter also includes:

[0153] The third storage module stores the position and size information of different functional areas around the unmanned scooter. The collection range of the environment collection module includes all functional areas, including the first-level deceleration area, the second-level deceleration area, the third-level deceleration area, and the stop area (different levels of deceleration areas have different final deceleration speeds); along the length direction of the unmanned scooter, the distance range between different functional areas and the front end of the unmanned scooter is different;

[0154] The acquisition module is used to obtain the operation information of the unmanned vehicle, and the operation information of the unmanned vehicle includes: the operation speed of the unmanned vehicle;

[0155] The fourth calculation module is used to calculate the minimum target distance between each functional area and the front end of the unmanned mobility scooter; when it is detected that the target moving obstacle enters the corresponding functional area, the main control module controls the driving module to execute the function of the corresponding functional area;

[0156]

[0157] B zB is the minimum target distance between the zth functional area and the front end of the unmanned vehicle along the length direction of the unmanned vehicle; z0 is the preset minimum distance between the zth functional area and the front end of the unmanned scooter along the length direction of the unmanned scooter; lg is the logarithm with base 10; min represents the minimum value; μ is the friction coefficient of the unmanned scooter on the current type of road surface; δ is the slip rate of the unmanned scooter on the current type of road surface; is the current speed of the autonomous vehicle; θ ty The shortest distance of the t-th target moving obstacle in the collection range of the environment collection module from the unmanned vehicle along the direction parallel to the width of the unmanned vehicle; is the tth target moving obstacle in the collection range of the environment collection module along θ ty The speed of movement in the direction The direction of movement is the positive direction; t dz The time required for the current unmanned vehicle to complete the function corresponding to the zth functional area; t 0 The reaction time of the autonomous vehicle to start executing the function corresponding to the zth functional area; for The corresponding distance adjustment coefficient (based on the preset --Get the distance adjustment coefficient mapping table, the distance adjustment coefficient is greater than 0 and less than 1); For combination The distance adjustment coefficient obtained (based on The distance adjustment coefficient mapping table is obtained, and the distance adjustment coefficient value is greater than 0 and less than 1).

[0158] The beneficial effects of the above technical solution are:

[0159] B z0 is the preset minimum distance between the zth functional area and the front end of the unmanned mobility scooter along the length direction of the unmanned mobility scooter, B z0 Corresponding to the standard road type, standard driving speed and standard target moving obstacle speed; but considering the differences in the actual standard road type, driving speed and target moving obstacle speed, a distance adjustment coefficient is determined based on the actual driving speed, the friction coefficient and slip rate corresponding to the actual road type, and a distance adjustment coefficient is determined based on the actual multiple target moving obstacles. A distance adjustment coefficient is determined, and the minimum target distance between the functional area and the front end of the unmanned mobility scooter is determined based on the above two distance adjustment coefficients. The size of each functional area is fixed. When the target moving obstacle is detected to enter the corresponding functional area, the main control module controls the drive module to execute the function of the corresponding functional area to ensure driving safety. Based on the above formula, the appropriate functional area range can be determined to ensure early response to take the corresponding function and ensure the reliability of executing the corresponding function.

[0160] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A smart shared control system for an intelligent unmanned vehicle, characterized in that: include: The environment acquisition module is used to collect obstacle information and terrain information around the unmanned vehicle; A positioning and navigation module is used to obtain the current position of the unmanned vehicle. The positioning and navigation module is configured with a high-precision map; A path generation module is used to generate a driving path for the unmanned vehicle based on a high-precision map, the current location of the unmanned vehicle, and the departure point and destination of the passenger; A driving module, which is used to control the driving of the unmanned vehicle according to the driving path of the unmanned vehicle and the information collected by the environment collection module; A communication module, the communication module is connected to the first terminal, the second terminal, and the cloud computing system for communication; A road surface type recognition module, used to identify the road surface type of the road on which the unmanned mobility scooter is traveling; The main control module is electrically connected to the environment acquisition module, the positioning and navigation module, the path generation module, the driving module, the communication module, and the road type identification module. The main control module is also electrically connected to the data processing system.

2. According to claim 1, the intelligent shared control system for an intelligent unmanned vehicle is characterized in that: The environment acquisition module includes a first camera module, a laser radar and an ultrasonic sensor; The first camera module is used to obtain visual information of the environment where the unmanned vehicle is located and provide image and video data, which are used for map construction, motion control and navigation tasks; LiDAR uses laser beams to measure the distance and position of obstacles around the self-driving car, generating accurate point cloud data, which can provide high-precision three-dimensional environmental perception, helping the self-driving car detect obstacles, build maps, measure distances and make obstacle avoidance decisions; Ultrasonic sensors measure the distance to obstacles by sending and receiving ultrasonic signals. They are used for close-range obstacle detection and avoidance to avoid collisions with static obstacles. The intelligent shared control system of the intelligent unmanned mobility scooter also includes: a lane recognition module for identifying the lane in which the unmanned mobility scooter is located.

3. The intelligent shared control system of an intelligent unmanned vehicle according to claim 1 is characterized in that: Also includes: The air pressure measurement module is used to collect the atmospheric pressure value where the unmanned mobility scooter is located and convert it into the corresponding atmospheric pressure altitude value; The positioning and navigation module is a global positioning system GPS module or a Beidou satellite navigation system, and also includes a near-orbit Starlink navigation system; Pavement types include: pavement material type and pavement condition type. Pavement condition types include: flat pavement and bumpy pavement. Pavement material types specifically include: asphalt pavement, gravel pavement, dirt road and sand.

4. The intelligent shared control system of an intelligent unmanned vehicle according to claim 1 is characterized in that: Also includes: The vehicle speed detection module is used to detect the current speed of the unmanned vehicle; The speed limit information acquisition module is used to obtain the maximum permissible speed of the unmanned mobility scooter on the current road section; The vehicle speed comparison module is used to compare the current driving speed of the unmanned vehicle with the maximum allowable driving speed of the current section of the road where the unmanned vehicle is located, and obtain the vehicle speed comparison result. The main control module controls the drive module to work according to the vehicle speed comparison result to control the driving speed of the unmanned vehicle to be no greater than the maximum allowable driving speed of the current section of the road where the unmanned vehicle is located.

5. The intelligent shared control system of an intelligent unmanned vehicle according to claim 1 is characterized in that: Also includes: An intelligent module for entertainment and office is arranged in the unmanned vehicle. The intelligent module for entertainment and office includes: an on-board laptop computer and a routing module. The routing module is used to provide Internet access capability for the on-board laptop computer.

6. The intelligent shared control system of an intelligent unmanned vehicle according to claim 1 is characterized in that: Also includes: An order processing module, used for receiving a taxi order sent by the first terminal and dispatching the taxi order to the second terminal; The vehicle location positioning module is used to locate the vehicle location based on the departure information in the taxi order information; The drop-off point positioning module is used to locate the drop-off point based on the destination information in the taxi order information; A fee generation module, used for generating and storing fee information according to the itinerary information between the passenger's departure place and the passenger's destination after the taxi order is completed at the second terminal; The fee sending module sends the fee information to the first terminal and the second terminal when the taxi order is displayed as completed. The first terminal completes the payment for the ride through the mobility scooter platform.

7. The intelligent shared control system of an intelligent unmanned vehicle according to claim 1 is characterized in that: The data processing system is used to process the information collected by the environment collection module to determine the movement speed and position of the target moving obstacle in the driving environment of the unmanned scooter; the target moving obstacle is a moving obstacle with a risk of colliding with the unmanned scooter in the moving direction, specifically a moving obstacle close to the unmanned scooter along the width direction of the unmanned scooter; The intelligent shared control system of the intelligent unmanned vehicle also includes: The third storage module stores the position and size information of different functional areas around the unmanned scooter. The collection range of the environment collection module includes all functional areas, including the first-level deceleration area, the second-level deceleration area, the third-level deceleration area, and the stop area. Along the length direction of the unmanned scooter, the distance range between different functional areas and the front end of the unmanned scooter is different; The acquisition module is used to obtain the operation information of the unmanned vehicle, and the operation information of the unmanned vehicle includes: the operation speed of the unmanned vehicle; The fourth calculation module is used to calculate the minimum target distance between each functional area and the front end of the unmanned mobility scooter; when it is detected that the target moving obstacle enters the corresponding functional area, the main control module controls the driving module to execute the function of the corresponding functional area; B z B is the minimum target distance between the zth functional area and the front end of the unmanned vehicle along the length direction of the unmanned vehicle; z0 is the preset minimum distance between the zth functional area and the front end of the unmanned scooter along the length direction of the unmanned scooter; lg is the logarithm with base 10; min represents the minimum value; μ is the friction coefficient of the unmanned scooter on the current type of road surface; δ is the slip rate of the unmanned scooter on the current type of road surface; is the current speed of the autonomous vehicle; θ ty The shortest distance of the t-th target moving obstacle in the collection range of the environment collection module from the unmanned vehicle along the direction parallel to the width of the unmanned vehicle; is the tth target moving obstacle in the collection range of the environment collection module along θ ty The speed of movement in the direction The direction of movement is the positive direction; t dz is the time required for the current unmanned vehicle to complete the function corresponding to the zth functional area; t0 is the reaction time of the unmanned vehicle to start executing the function corresponding to the zth functional area; for The corresponding distance adjustment coefficient; For combination The obtained distance adjustment coefficient.

8. The intelligent shared control system of an intelligent unmanned vehicle according to claim 1 is characterized in that: It also includes a battery power management module, which includes a battery pack, a charging module, a charging and discharging chip and a power detection chip; the power detection chip includes a CPU, a voltage monitoring unit, a current monitoring unit, a battery status detection unit and a bus communication unit.

9. The intelligent shared control system of the intelligent unmanned vehicle according to claim 8 is characterized in that: Also includes: The third acquisition module obtains a power change curve of the remaining power of the unmanned vehicle after the last E times of charging as a function of usage time; A screening and marking unit is used to obtain the sub-curve segment where the unmanned vehicle is traveling in the power change curve, and mark the corresponding road segment type in the sub-curve segment corresponding to different types of road segments, and the road segment types include: flat road segment type, first-level bumpy road segment type, second-level bumpy road segment type, and third-level bumpy road segment type; The first storage module stores the travel speed of the unmanned vehicle passing through the bumpy road section within a recent preset time period and the bumpiness level of the bumpy road section; The transport task acquisition module is used to acquire the information of the current transport task, and the information of the current transport task includes: the type and length of the road section in the driving path of the unmanned vehicle executing the current transport task; A second calculation module is used to calculate a first availability of each unmanned mobility scooter that is currently idle and can perform a current transportation task; S r1 is the first availability of the rth unmanned mobility vehicle that is currently idle and can perform the current transportation task; H is the total number of road segment types on the driving path of the unmanned mobility vehicle that performs the current transportation task; L h is the length of the hth type of road section in the driving path of the unmanned vehicle performing the current transportation task; v hr The average driving speed of the rth unmanned mobility vehicle that is currently idle and can perform the current transportation task and passes through the hth type of road section obtained based on the first storage module; Y r The total number of sub-curve segments of the h-th road segment type selected for the calculation of the r-th autonomous vehicle that is currently idle and can perform the current transportation task; G rhy The amount of power reduction of the selected sub-curve segment of the yth hth road segment type for the calculation of the rth unmanned mobility vehicle that is currently idle and can perform the current transportation task; t rhy The driving time of the selected sub-curve segment of the yth hth road segment type for the calculation of the rth unmanned mobility vehicle that is currently idle and can perform the current transportation task; K rhy1 K is the average value of the absolute value of the inflection point of the yth sub-curve segment of the hth road segment type selected for the calculation of the rth unmanned mobility vehicle that is currently idle and can perform the current transportation task; rhy2 The maximum absolute value of the slope of the inflection point of the yth sub-curve segment of the hth road segment type selected for the calculation of the rth unmanned mobility vehicle that is currently idle and can perform the current transportation task; G r is the remaining power of the rth unmanned mobility vehicle that is currently idle and can perform the current transportation task; max represents the maximum value; ln is the natural logarithm, and e is a natural constant; The primary screening module is used to select an unmanned vehicle that is currently idle and can perform the current transportation task and has an availability greater than a preset availability as the first unmanned vehicle.

10. The intelligent shared control system of the intelligent unmanned vehicle according to claim 9 is characterized in that: Also includes: The second storage module further stores vibration information detected by a second vibration sensor of the first unmanned vehicle passing through a bumpy road section within a preset time period, and the second vibration sensor is arranged in the cargo compartment of the first unmanned vehicle; A third calculation module, configured to calculate a second availability of each first unmanned mobility vehicle; S r2 is the second availability of the rth first unmanned mobility vehicle; x vur1(t) is the vibration acceleration signal acquired by the second vibration sensor when the rth first unmanned mobility vehicle passes the bumpy road section of the vth level for the uth time and stored in the second storage module; T2 is the sampling time of the vibration acceleration signal corresponding to the second vibration sensor; The vibration frequency of the rth first unmanned mobility vehicle obtained by the second vibration sensor when the rth first unmanned mobility vehicle passes through the bumpy road section of the vth level for the uth time stored by the second storage module; vur1 is the maximum amplitude obtained by the second vibration sensor when the rth first unmanned mobility vehicle passes the bumpy road section of the vth level for the uth time stored in the second storage module; g is the acceleration of gravity; ∝1 and ∝2 are the first vibration evaluation coefficient and the second vibration evaluation coefficient respectively; F vur is the equivalent vibration parameter acquired by the second vibration sensor based on the second storage module when the rth first unmanned mobility vehicle passes the bumpy road section of the vth level for the uth time; o1 is the maximum allowable equivalent vibration parameter of the current transportation task; o 1v is the preset reference vibration evaluation value of the bumpy road section of level v; rv The number of bumpy sections of the vth level determined by the rth first unmanned mobility vehicle based on the information stored in the second storage module Greater than P 1v The number of times; P 1v For the bumpy road section of level v Corresponding preset reference value; H 1rv is the total number of times the rth first unmanned mobility vehicle passes through the vth level bumpy road section determined based on the information stored in the second storage module; R rv is the vibration evaluation value of the rth first unmanned mobility vehicle passing through the vth level bumpy road section; max represents the maximum value; a third calculation module, configured to calculate the comprehensive availability of each first unmanned mobility scooter based on the second calculation module and the third calculation module, select a first unmanned mobility scooter whose comprehensive availability is greater than a preset comprehensive availability as a target first unmanned mobility scooter, and use the target first unmanned mobility scooter to perform the current transportation task; S r =∈1S r1 +∈2S r2 ; S r is the comprehensive availability of the rth unmanned mobility vehicle, ∈1 and ∈2 are the first availability weight and the second availability weight, respectively.

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