Intelligent control method and system for two-wheeled vehicle and application of intelligent control method and system

Abstract

The invention provides an intelligent control method and system for a two-wheeled vehicle and application of the intelligent control method and system. The method comprises the steps that the two-wheeled vehicle is monitored in real time; when the two-wheeled vehicle enters the first area, the main control device controls the two-wheeled vehicle to realize automatic driving in the first area; a follow-up stroke of the two-wheeled vehicle after the two-wheeled vehicle leaves the current area is obtained, and if a second area is involved in the follow-up stroke, an intelligent control chain is obtained and sent to the auxiliary control device; when the two-wheeled vehicle drives into the second area, the auxiliary control device is controlled to execute the intelligent control chain, and automatic driving of the two-wheeled vehicle in the second area is achieved. According to the two-wheeled vehicle, efficient automatic driving in a single area can be achieved, seamless switching and intelligent control can be achieved among different areas, and therefore the intelligent level and practicability of the two-wheeled vehicle are remarkably improved. A complex processing system is moved to the outside, electronic components on the two-wheeled vehicle can be greatly simplified, and the weight and the size of vehicle-mounted hardware are remarkably reduced.

Description

Technical Field

[0001] The present application relates to the field of intelligent driving of two-wheeled vehicles, and more specifically, to an intelligent control method and system for a two-wheeled vehicle and applications thereof. Background Art

[0002] With the acceleration of urbanization, traffic congestion and environmental pollution have become severe challenges faced by major cities around the world. As an environmentally friendly and convenient means of transportation, two-wheeled vehicles are becoming more and more popular. However, traditional two-wheeled vehicles still rely on manual operation, which not only limits their application in complex traffic environments, but also fails to fully realize their potential as intelligent transportation tools. In recent years, with the rapid development of artificial intelligence, Internet of Things technology and sensor technology, intelligent control technology has been gradually applied to the field of two-wheeled vehicles in order to improve their safety, efficiency and user experience.

[0003] Although the existing intelligent control schemes for two-wheeled vehicles have improved the safety and convenience of riding to a certain extent, there are still some obvious drawbacks. For example, most of the existing intelligent control methods for two-wheeled vehicles focus on automated control within a single area, and lack the ability to coordinate and control multiple areas intelligently. In addition, the driving environment of two-wheeled vehicles is more complex than that of cars, and a processing system with high computing power is required to cope with such complex intelligent driving. Once a complex computing device is applied to a two-wheeled vehicle, the size of the two-wheeled vehicle will become large and lose its flexible and convenient characteristics. In addition, existing technologies often fail to make full use of environmental data for real-time adjustments, making two-wheeled vehicles less responsive when facing dynamically changing traffic conditions. Summary of the invention

[0004] Based on the problems existing in the prior art, this application provides an intelligent control method, system and application of a two-wheeled vehicle. The specific solution is as follows: In the first part, the present application proposes an intelligent control method for a two-wheeled vehicle, including the following: Real-time monitoring of two-wheeled vehicles entering the preset area; When a two-wheeled vehicle enters the first area, the preset main control device is controlled to send an intelligent control request to the two-wheeled vehicle, and after the request is passed, the auxiliary control device pre-installed on the two-wheeled vehicle is connected to control the automatic driving of the two-wheeled vehicle; The sensor device integrated on the two-wheeled vehicle detects environmental data around the two-wheeled vehicle, so that the main control device controls the operation of the two-wheeled vehicle according to the environmental data, thereby realizing automatic driving of the two-wheeled vehicle in the first area; Obtaining a subsequent trip of the two-wheeled vehicle after leaving the current area, and if the subsequent trip involves a preset second area, obtaining a pre-configured intelligent control chain for the second area and sending it to the auxiliary control device; When the two-wheeled vehicle enters the second area, the auxiliary control device is controlled to execute the intelligent control chain, and the operation of the two-wheeled vehicle is adjusted in combination with the environmental data detected by the sensor device, so as to realize automatic driving of the two-wheeled vehicle in the second area.

[0005] In some specific embodiments, it also includes: Determine whether there is pre-entered travel data: If so, determining a subsequent trip after leaving the first area according to the trip data; If not present, the subsequent trip is predicted based on the driving path of the two-wheeled vehicle in the first area or the second area.

[0006] In some specific embodiments, all possible trips of the two-wheeled vehicle after leaving the first area or the second area are predicted based on the road map of the first area and the driving path of the two-wheeled vehicle in the first area or the second area to obtain a trip prediction result; Continuously adjusting the travel prediction result according to the travel path of the two-wheeled vehicle; After determining the departure area where the two-wheeled vehicle leaves the first area or the second area, one or more first areas and second areas corresponding to the departure area are screened out to obtain a final travel prediction result.

[0007] In some specific embodiments, it also includes: Acquire a road where two-wheeled vehicles are allowed to travel, and divide the road into one or more sub-areas; Determine the population density, traffic flow and infrastructure information in each sub-area and road; Excluding the areas where the population distribution density is greater than a preset density and the areas where the traffic flow is greater than a preset flow in each sub-area, to obtain each candidate area; Analyze the facility perfection of the road according to the infrastructure information, exclude the part of the road where the population distribution density is greater than the preset density, the part of the road where the traffic flow is greater than the preset flow, and the part of the road where the facility perfection is lower than the preset perfection, to obtain multiple discrete candidate roads; All candidate areas and candidate roads that can be connected to each first road are divided into the same area group, and the candidate areas and candidate roads in each area group are integrated to obtain each first area.

[0008] In some specific embodiments, it also includes: taking all candidate roads except those in the first area as quasi-roads; Acquire in advance the road data of each quasi-road and the driving data of the two-wheeled vehicle when driving on each quasi-road; A basic control chain for the auxiliary control device is constructed according to the road data, and the basic control chain is iteratively trained using the driving data as training data to obtain the intelligent control chain.

[0009] In some specific embodiments, each first area is pre-configured with one or more main control devices, and each main control device can control one or more two-wheeled vehicles at the same time; When there is a main control device in a first area, the main control device is located at the geometric center of the first area, and the control range is larger than the first area; When there are multiple main control devices in a first area, each main control device is distributed in different areas of the first area according to its own control range, and the multiple main control devices are communicatively connected.

[0010] In some specific embodiments, the first area includes a road portion and a non-road portion, the second area includes only a road portion, and part of the road portion of the second area is distributed around the first area.

[0011] In the third part, the present application proposes an intelligent control system for a two-wheeled vehicle, including the following: A position detection unit, used for real-time monitoring of two-wheeled vehicles entering a preset area; A first area unit is used to control a preset main control device to send an intelligent control request to the two-wheeled vehicle when a two-wheeled vehicle enters the first area, and to access an auxiliary control device pre-installed on the two-wheeled vehicle after the request is passed, and to control the automatic driving of the two-wheeled vehicle; The sensor device integrated on the two-wheeled vehicle detects environmental data around the two-wheeled vehicle, so that the main control device controls the operation of the two-wheeled vehicle according to the environmental data, thereby realizing automatic driving of the two-wheeled vehicle in the first area; A second area unit, used for obtaining a subsequent trip of the two-wheeled vehicle after leaving the current area, and if the subsequent trip involves a preset second area, obtaining a pre-configured intelligent control chain of the second area and sending it to the auxiliary control device; When the two-wheeled vehicle enters the second area, the auxiliary control device is controlled to execute the intelligent control chain, and the operation of the two-wheeled vehicle is adjusted in combination with the environmental data detected by the sensor device, so as to realize automatic driving of the two-wheeled vehicle in the second area.

[0012] In some embodiments, it also includes: The travel unit is used to determine whether there is pre-entered travel data: If so, determining a subsequent trip after leaving the first area according to the trip data; If not present, the subsequent trip is predicted based on the driving path of the two-wheeled vehicle in the first area or the second area.

[0013] In the third part, the present application proposes a two-wheeled vehicle, including a vehicle body, a main control device, and an auxiliary control device, which is used to implement an intelligent control method for a two-wheeled vehicle as described in any one of the first parts.

[0014] Beneficial effects: The present application proposes an intelligent control method, system and application of a two-wheeled vehicle, which can not only realize efficient automatic driving in a single area, but also realize seamless switching and intelligent control between different areas, thereby significantly improving the intelligence level and practicality of the two-wheeled vehicle. By moving the complex processing system to the outside, the electronic components on the two-wheeled vehicle can be greatly simplified, and only the basic processing unit, sensor, actuator and communication module need to be retained, which can significantly reduce the weight and volume of the on-board hardware, making the two-wheeled vehicle lighter and easier to operate. By detecting the surrounding environment data in real time through the integrated sensor device and controlling the operation of the two-wheeled vehicle based on these data, the occurrence of collision accidents can be effectively reduced, especially the automatic driving in the first area and the second area, which helps to improve the safety of riding. By automatically identifying and responding to entering a specific area (such as the first area) and autonomously requesting access to the main control device, a highly automated driving experience is achieved, reducing the need for manual intervention by the user. When the two-wheeled vehicle enters different preset areas (such as the second area), the system can automatically execute the corresponding intelligent control chain, further improving the ability of automated driving.

[0015] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 It is a flow chart of the intelligent control method of the present application; Figure 2 is a schematic diagram of the hierarchical relationship between the features of this application; Figure 3 It is a schematic diagram of the control principle of this application; Figure 4 It is a schematic diagram of the intelligent control system module of this application.

[0018] Reference numerals: 1 - position detection element; 2 - first area unit; 3 - second area unit. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0020] This application proposes an intelligent control method for a two-wheeled vehicle, which can not only realize efficient automatic driving in a single area, but also realize seamless switching and intelligent control between different areas, thereby significantly improving the intelligence level and practicality of the two-wheeled vehicle, and providing a more flexible, safe and efficient solution for future urban transportation. The flow diagram of the intelligent control method for a two-wheeled vehicle is shown in the attached figure. Figure 1 The control principle is shown in the attached Figure 2 The specific plan is as follows: An intelligent control method for a two-wheeled vehicle, comprising the following steps: 101. Real-time monitoring of two-wheeled vehicles entering the preset area; 102. When a two-wheeled vehicle enters the first area, the main control device is controlled to send an intelligent control request to the two-wheeled vehicle, and after the request is passed, the auxiliary control device pre-installed on the two-wheeled vehicle is connected to control the automatic driving of the two-wheeled vehicle; the environmental data around the two-wheeled vehicle is detected by the sensor device integrated on the two-wheeled vehicle, so that the main control device controls the operation of the two-wheeled vehicle according to the environmental data, and realizes the automatic driving of the two-wheeled vehicle in the first area; 103. Obtain the subsequent trip of the two-wheeled vehicle after leaving the current area. If the subsequent trip involves a preset second area, obtain the pre-configured intelligent control chain of the second area and send it to the auxiliary control device; when the two-wheeled vehicle enters the second area, control the auxiliary control device to execute the intelligent control chain, and adjust the operation of the two-wheeled vehicle in combination with the environmental data detected by the sensor device to realize the automatic driving of the two-wheeled vehicle in the second area. In this application, two-wheeled vehicles include electric bicycles, electric motorcycles, motorcycles and other driving tools with circuits, but two wheels do not limit the driving tool to have only two wheels, but two driving wheels.

[0021] The solution of the present application can realize the seamless connection and automatic driving of two-wheeled vehicles between different preset areas without manual intervention, thereby improving the intelligence level and user experience of two-wheeled vehicles. Among them, the main control device is located in the first area, has powerful computing and processing capabilities, and can remotely control the automatic driving of multiple two-wheeled vehicles. This distributed architecture can effectively reduce the computing burden of the auxiliary control device on a single two-wheeled vehicle and improve the processing efficiency of the overall system. The auxiliary control device is located on the two-wheeled vehicle, has weak processing capabilities, and can control the automatic driving of the two-wheeled vehicle according to the intelligent control chain preset in the second area. The sensor device on the two-wheeled vehicle can detect the surrounding environment data in real time, and the auxiliary control device adjusts the operation of the two-wheeled vehicle according to these data to ensure safety and efficiency in a dynamically changing traffic environment. The sensor device on the two-wheeled vehicle can detect the surrounding environment data in real time, and the auxiliary control device adjusts the operation of the two-wheeled vehicle according to these data to ensure safety and efficiency in a dynamically changing traffic environment. The combination of a powerful main control device and real-time environmental perception can respond faster in an emergency and improve the safety and reliability of the system.

[0022] In the present application, the main control device is set as a fixed signal tower located outside the two-wheeled vehicle and within the first area, rather than being integrated into the two-wheeled vehicle, which takes advantage of modern communication technology and cloud computing, can greatly optimize the design and performance of the two-wheeled vehicle, and brings significant advantages in reducing the load of the two-wheeled vehicle itself and improving system efficiency. By moving the complex processing system to the outside, the electronic components on the two-wheeled vehicle can be greatly simplified, and only the basic processing unit, sensor, actuator and communication module need to be retained, which can significantly reduce the weight and volume of the on-board hardware, making the two-wheeled vehicle lighter and easier to operate. The computing resources required for processing intensive tasks (such as environmental perception, path planning, etc.) consume a lot of electricity. Transferring these tasks to the cloud or fixed signal towers can greatly reduce the burden on the battery of the two-wheeled vehicle, thereby extending its cruising range. Fixed signal towers can be equipped with more powerful computing resources and can handle complex algorithms and big data analysis, which is particularly important for tasks such as real-time path planning, environmental perception and safety monitoring. Through cloud services, the main control device can be easily updated with software and upgraded in terms of functions without having to operate each two-wheeled vehicle one by one, which improves the maintainability and flexibility of the system. In urban environments, fixed signal towers can be deployed at key locations, such as transportation hubs, commercial areas, or residential areas, to cover a wide area. In closed or semi-closed environments, such as industrial parks or university campuses, fixed signal towers can provide precise vehicle management and dispatching. In scenic areas, signal towers can help manage two-wheeled vehicles used by tourists, provide route guidance and safety guarantees. In practical applications, the location and number of signal towers are reasonably planned to ensure that two-wheeled vehicles in each first area can obtain good signal coverage. And set up backup signal towers or adopt multi-path communication solutions as needed to prevent single point failures.

[0023] Step 101 involves real-time monitoring of two-wheeled vehicles entering a preset area. This is the first step in the entire intelligent control process, ensuring that only two-wheeled vehicles entering a specific area will be included in the intelligent control range. It is necessary to clarify the geographical boundaries of the first area, which can be a block, a park, a university campus or other specific areas. The boundaries of the first area can be accurately marked on the map, and a geographic information system (GIS) can be used to draw and manage these boundaries. In some embodiments, multiple detection devices and positioning devices are used to detect whether a two-wheeled vehicle enters the first area. The detection device includes a camera and an RFID reader, a Bluetooth beacon, and the positioning device includes a GPS module to perform multi-source data comparison to improve the accuracy of the detection. When at least one detection device detects a two-wheeled vehicle, and the positioning device shows that the position of the two-wheeled vehicle is close to the first area, it is determined that the two-wheeled vehicle has entered the first area.

[0024] In practical applications, a high-definition camera can be installed at the entrance of the area to ensure that all entry paths are covered, and a computer vision algorithm (such as OpenCV, TensorFlow, etc.) is used to process the images captured by the camera to identify the two-wheeled vehicle. The recognition result is sent to the main control device through the network (such as 4G / 5G, Wi-Fi). RFID tags can also be installed on the two-wheeled vehicle, and RFID readers can be installed at the entrance of the area to ensure that the RFID tags on all the two-wheeled vehicles entering can be read. The read tag information is sent to the main control device through the network (such as 4G / 5G, Wi-Fi). A Bluetooth module can also be installed on the two-wheeled vehicle, and a Bluetooth beacon can be installed at the entrance of the area to ensure that it can communicate with the Bluetooth module on the two-wheeled vehicle entering. The detected signal information is sent to the main control device through the network (such as 4G / 5G, Wi-Fi). In some embodiments, the GPS module of the two-wheeled vehicle is used to determine whether to enter the first area through location data. Assuming that the intelligent control of the two-wheeled vehicle is realized on a university campus, the specific steps are as follows: 1. Define the geographical location of the first area: the main entrance and surrounding roads of the university campus. Marking: Accurately mark the boundaries of the campus entrance and surrounding roads on the map. 2. Deploy detection devices Camera: Install high-definition cameras at the entrance of the campus to cover all entry paths. RFID reader: Install RFID readers at the entrance of the campus. Bluetooth beacon: Install Bluetooth beacons at the entrance of the campus. GPS: Ensure that the GPS module on the two-wheeled vehicle is working properly. The autonomous control device receives data from the camera, RFID reader, Bluetooth beacon and GPS, and parses the data to confirm whether the two-wheeled vehicle has entered the first area. The information of the two-wheeled vehicle entering the first area is recorded and stored in the database. Once it is confirmed that the two-wheeled vehicle has entered the campus, the main control device sends an intelligent control request to the two-wheeled vehicle, preparing to access the auxiliary control device and control the automatic driving of the two-wheeled vehicle.

[0025] In order to ensure the effectiveness and safety of the two-wheeled vehicle intelligent control system, the first area needs to be carefully selected and defined. The following is a detailed first area acquisition process, including obtaining roads that allow two-wheeled vehicles to travel, dividing sub-areas, screening candidate areas and integrating area groups to finally determine the first area. In some specific embodiments, it also includes: obtaining roads that allow two-wheeled vehicles to travel, and dividing one or more sub-areas along the road; determining the population distribution density, traffic flow and infrastructure information on each sub-area and road; excluding areas where the population distribution density is greater than the preset density and the traffic flow is greater than the preset flow in each sub-area, and obtaining each candidate area; parsing the facility perfection on the road according to the infrastructure information, excluding the part of the road where the population distribution density is greater than the preset density, the part where the traffic flow is greater than the preset flow, and the part where the facility perfection is lower than the preset perfection, to obtain multiple discrete candidate roads; dividing all candidate areas and the candidate roads that can be connected to each first road into the same area group, and integrating the candidate areas and candidate roads in each area group to obtain each first area.

[0026] Use city traffic maps, traffic planning data, road attribute databases, etc. to select roads that allow two-wheeled vehicles to travel and exclude roads that prohibit two-wheeled vehicles (such as highways, certain main roads, etc.). Select areas that follow traffic rules to ensure that two-wheeled vehicles can travel according to preset traffic rules and reduce violations. Select roads that allow two-wheeled vehicles to travel and ensure that the conditions of these roads are suitable for autonomous driving. Select areas with sufficient data support, such as traffic monitoring data, demographic data, etc., for more accurate analysis and control. For example, flat, obstacle-free roads are easier to achieve autonomous driving.

[0027] Divide the road into several sub-areas according to the road length, traffic nodes (such as intersections, parking lot entrances, etc.) and geographical features (such as parks, schools, commercial areas, etc.). The size of the sub-area can be flexibly adjusted according to actual needs. Usually, the length of each sub-area ranges from a few hundred meters to several kilometers. By dividing the road into multiple sub-areas, the traffic conditions in each area can be managed and controlled more finely, and the controllability of the system can be improved. Demographic data, mobile communication data, social media check-in data, etc. can be used to determine the population distribution density in each sub-area. The traffic flow of each sub-area is obtained through traffic monitoring systems, traffic flow counters, historical traffic data, etc. Collect road facility data such as traffic lights, traffic signs, bicycle lanes, crosswalks, intelligent transportation systems (ITS), etc. to obtain infrastructure information.

[0028] According to safety and feasibility requirements, thresholds for crowd density and traffic flow are set. Exclusion conditions: sub-areas with crowd density greater than the preset density. Sub-areas with traffic flow greater than the preset flow. After excluding sub-areas that do not meet the conditions, the remaining sub-areas are used as candidate areas. Assess the completeness of infrastructure in each sub-area, including traffic lights, traffic signs, bicycle lanes, crosswalks, etc. Areas with high crowd density may have more pedestrians and non-motor vehicles, increasing the risk of collisions and accidents. By excluding areas with crowd density greater than the preset density, potential safety hazards can be reduced. Areas with high traffic flow mean more motor vehicles, increasing the frequency of interaction between two-wheeled vehicles and motor vehicles, thereby increasing the risk of accidents. This risk can be reduced by excluding areas with traffic flow greater than the preset flow.

[0029] Set a threshold for infrastructure perfection. Exclusion conditions: Areas where the population density is greater than the preset density. Areas where the traffic flow is greater than the preset flow. Areas where the facility perfection is lower than the preset perfection. After excluding areas that do not meet the conditions, the remaining areas are used as candidate roads. Perfect traffic infrastructure (such as traffic lights, traffic signs, bicycle lanes, etc.) can provide better traffic management and guidance and improve safety. By excluding areas where the infrastructure perfection is lower than the preset perfection, it can ensure that two-wheeled vehicles travel in an environment with good traffic management.

[0030] For example, the part where the population density is greater than 100 people / square kilometer. The part where the traffic flow is greater than 1,000 vehicles / hour. The part where the facility completeness is less than 80%. In some embodiments, areas with a traffic flow greater than 1,000 vehicles / hour are excluded, and these areas are considered high-risk areas. Areas with a traffic flow less than 1,000 vehicles / hour are retained, and these areas are considered relatively safe areas. By excluding high-traffic flow areas, the frequency of interaction between two-wheeled vehicles and motor vehicles can be reduced, and the risk of accidents can be reduced. In addition, the traffic conditions in low-traffic flow areas are relatively simple, the driving difficulty and risk of two-wheeled vehicles are lower, the data processing pressure is relatively small, and the communication network is relatively unobstructed, which helps to improve the response speed of the system and the timeliness of control. The control strategy in low-traffic flow areas is relatively simple, which can reduce the complexity of the system and improve the reliability and stability of the system. In addition, the difficulty of environmental perception in low-traffic flow areas is lower, and sensors can more accurately identify and process targets, reducing the risk of misjudgment and missed detection.

[0031] Analyze the connectivity of each candidate road with other candidate areas, and divide the candidate areas and candidate roads that can be directly or indirectly connected into the same area group. Each area group contains one or more candidate roads and their connected candidate areas. Integrate the candidate areas and candidate roads in each area group to form a complete area. The integrated area is the first area. Integrating the connected candidate areas and candidate roads into the same area group can form a coherent control area, which is convenient for unified management and scheduling.

[0032] Exemplarily, it specifically includes: obtaining traffic network data, infrastructure data and geographic data. Traffic network data includes road network, road attributes (such as allowing two-wheeled vehicles to drive), traffic flow, crowd distribution density, etc. Infrastructure data includes traffic lights, traffic signs, bicycle lanes, etc. Geographic data includes map data, administrative divisions, geographic boundaries, etc. According to the traffic network data, filter out roads that allow two-wheeled vehicles to drive. Divide the road into multiple sub-areas according to the road length and important traffic nodes (such as intersections, parking lot entrances, etc.). According to the crowd distribution density and traffic flow data, exclude those sub-areas and road segments that exceed the preset threshold. Model the road network as a graph structure, in which nodes represent intersections or important traffic nodes, and edges represent road segments. Set a weight for each edge, which can be an indicator such as road length, traffic flow, and infrastructure perfection. Use the connected component algorithm in graph theory (such as depth-first search DFS or breadth-first search BFS) to find all interconnected sub-areas and road segments. Take each connected component as a region group, and each region group contains one or more connected candidate regions and road segments. For adjacent regional groups, if they have strong connectivity (such as sharing more road segments), you can consider merging them into larger regional groups. Ensure that the roads and regions within each regional group are connected and meet all preset conditions (such as traffic flow, crowd density, infrastructure perfection, etc.). After connectivity analysis and regional group integration, each regional group is a first region. The finalized first region data is output for subsequent intelligent control system deployment.

[0033] Step 102 involves sending an intelligent control request to the two-wheeled vehicle after confirming that the two-wheeled vehicle has entered the preset first area, preparing to access the auxiliary control device and control the automatic driving of the two-wheeled vehicle. This step is a key link in the entire intelligent control process, ensuring that the two-wheeled vehicle can smoothly accept the instructions of the intelligent control system and realize automatic driving. The main control device generates an intelligent control request, requesting the two-wheeled vehicle to access the auxiliary control device and prepare to accept automatic control. Select a suitable communication protocol (such as 4G / 5G, Wi-Fi, Bluetooth, etc.) to send the intelligent control request. The data format of the intelligent control request needs to meet the requirements of the two-wheeled vehicle control system, usually including control instructions, identity authentication information, encrypted data, etc. The receiving device on the two-wheeled vehicle (such as a 4G / 5G module, a Wi-Fi module, a Bluetooth module, etc.) receives the intelligent control request from the main control device. Data can also be received through the auxiliary control device. The main control device and the auxiliary control device are pre-configured with communication protocols, and can respond and interact quickly. The auxiliary control device can decode the received data, extract control instructions and other related information, and verify the identity of the intelligent control request to ensure that the request comes from a legitimate main control device. When the verification is passed, the two-wheeled vehicle switches to the automatic control mode and reports the current status to the main control device. The environmental perception device (such as camera, lidar, ultrasonic sensor, etc.) is started to obtain the surrounding environment information.

[0034] In this application, once the master control device confirms that the two-wheeled vehicle has entered the first area, it generates an intelligent control request. This request contains control instructions, identity authentication information, and encrypted data. The data format of the request must meet the requirements of the two-wheeled vehicle control system, which usually includes control instructions, identity authentication information, encrypted data, etc. The two-wheeled vehicle control system verifies the identity of the intelligent control request through a pre-shared key or certificate to ensure that the request comes from a legitimate master control device.

[0035] The main control device generates specific control instructions, such as steering angle, acceleration, deceleration, etc., based on the preset path planning algorithm and combined with the environmental perception data. The path planning algorithm may include the Dijkstra algorithm, etc. The main control device sends the control instructions to the actuator (such as the motor, steering system, etc.) to start automatic driving. The two-wheeled vehicle control system continuously receives feedback data from the sensor device and adjusts the control instructions in real time to ensure that the two-wheeled vehicle drives safely along the predetermined path. The main control device can remotely control the two-wheeled vehicle entering the first area to achieve automatic driving, ensuring the safety, reliability and efficiency of the system.

[0036] In some specific embodiments, it also includes: determining whether there is pre-input itinerary data: if there is, determining the subsequent itinerary after leaving the first area according to the itinerary data; if not, predicting the subsequent itinerary according to the driving path of the two-wheeled vehicle in the first area or the second area. If the system can obtain the user's pre-input itinerary data, it can plan the user's riding route more accurately and provide personalized services. If there is no pre-input itinerary data, the system can still predict the user's subsequent itinerary by analyzing the user's current riding path. Users can pre-input their itinerary data, such as the starting point, the end point, the expected arrival time, etc. through a mobile phone application or a vehicle interface. The main control device plans one or more optimal routes based on the pre-input itinerary data, combined with real-time traffic conditions, weather conditions and the user's personal preferences. Multiple routes can be recommended for users to choose, including the fastest route, the safest route or the most scenic route.

[0037] In some specific embodiments, all possible trips of the two-wheeled vehicle after leaving the first area or the second area are predicted based on the road map of the first area and the driving path of the two-wheeled vehicle in the first area or the second area to obtain a trip prediction result; the trip prediction result is continuously adjusted according to the driving path of the two-wheeled vehicle; after determining the departure area of ​​the two-wheeled vehicle leaving the first area or the second area, one or more first areas and second areas corresponding to the departure area are screened to obtain the final trip prediction result. Real-time location data is collected through the GPS module on the two-wheeled vehicle. The driving trajectory of the two-wheeled vehicle is recorded, including speed, acceleration, and steering angle. Environmental data around the two-wheeled vehicle is collected using sensors (such as cameras, radars, and laser radars). Check whether there is pre-entered trip data by the user, such as a destination entered through a mobile application. If there is pre-entered trip data, the system can directly use this data to plan subsequent trips. If there is no pre-entered trip data, the system needs to predict the subsequent trip based on the current driving path and other factors.

[0038] The prediction scheme includes: using a path matching algorithm (such as dynamic time warping DTW) to compare the current driving path with the historical path and find the most similar historical path. Based on the most similar historical path, the possible destination is inferred. If there are multiple possible destinations, these destinations are sorted according to factors such as time and frequency, and the most suitable one is selected as the prediction result. It is also possible to calculate the path from the current location to all possible destinations based on the road map of the first area or the second area. A graph search algorithm (such as Dijkstra algorithm or A* algorithm) can be used to find the shortest path or the optimal path.

[0039] Furthermore, as the two-wheeled vehicle moves, the main control device needs to continuously update the current position and driving path. If the two-wheeled vehicle deviates from the initially predicted path, the main control device should be able to re-evaluate and adjust the subsequent journey. If there are multiple feasible paths, the main control device can provide several options and recommend the best path based on user preferences (such as fastest, safest, and most power-efficient). Through the above steps, the main control device can effectively predict the journey of the two-wheeled vehicle after leaving the first area or the second area, and dynamically adjust it according to the real-time situation. Such a main control device not only improves the safety of the two-wheeled vehicle, but also improves the user experience.

[0040] The GPS positioning is used to determine whether the two-wheeled vehicle has left the current area. When the two-wheeled vehicle enters a new area, it is identified whether the area is the preset second area. If it is the preset second area, the intelligent control chain of the area is loaded, including the driving rules, speed limits, etc. of the specific area. When the two-wheeled vehicle enters the second area, the auxiliary control device starts to execute the loaded intelligent control chain. Combined with real-time environmental data, the auxiliary control device adjusts the speed, steering and other operations of the two-wheeled vehicle to ensure safe driving. When the two-wheeled vehicle is about to enter the second area, the main control device will send the corresponding intelligent control chain to the auxiliary control device on the two-wheeled vehicle. The auxiliary control device will use various sensors installed on the two-wheeled vehicle (such as cameras, radars, laser radars, etc.) to detect the surrounding environmental data in real time. According to the environmental data collected in real time, the auxiliary control device can dynamically adjust the operation of the two-wheeled vehicle. For example, if a pedestrian is detected in front, the auxiliary control device will immediately slow down or stop. The auxiliary control device will make the best driving decision based on the intelligent control chain and real-time environmental data to ensure the safe driving of the two-wheeled vehicle in the second area. The auxiliary control device controls the automatic driving of the two-wheeled vehicle in the second area based on the intelligent control chain and real-time environmental data.

[0041] In this application, the smart control chain is a set of predefined control instructions designed based on the characteristics and rules of the second area to ensure that the two-wheeled vehicle can travel safely and efficiently in the area. The smart control chain may contain information such as speed limits, prohibited areas, recommended driving routes, etc. These smart control chains will be stored in the cloud or local database and can be updated according to changes in the area.

[0042] In some specific embodiments, it also includes: treating all candidate roads except the first area as quasi-roads; obtaining in advance the road data of each quasi-road and the driving data of the two-wheeled vehicle when traveling on each quasi-road; constructing a basic control chain for the auxiliary control device based on the road data, and iteratively training the basic control chain with the driving data as training data to obtain an intelligent control chain. An intelligent control chain is a control strategy that combines road data, driving data of a two-wheeled vehicle, and a machine learning algorithm to create a control strategy that can adapt to a specific road environment. First, exclude the roads in the first area from all possible roads because the intelligent control chain is designed for the two-wheeled vehicle to travel after leaving the first area. All remaining roads are regarded as quasi-roads. These quasi-roads include roads in the second area and roads connecting the first area and the second area.

[0043] Collect static data for each road, including but not limited to: Road type: such as main road, secondary road, branch road, etc.

[0044] Road width: the number of lanes and the width of each lane.

[0045] Speed ​​Limit: The speed limit for each section of road.

[0046] Traffic signals: location and duty cycle of traffic lights.

[0047] Intersection information: intersection type (T-shaped, cross-shaped, etc.) and traffic rules.

[0048] Special signs: such as no-entry zones, pedestrian crossings, bicycle lanes, etc.

[0049] Collect real-time or historical dynamic data, including: Traffic flow: the traffic volume in each time period.

[0050] Traffic incidents: such as traffic accidents, road construction, etc.

[0051] Weather conditions: the impact of rain, snow, fog and other weather conditions on roads.

[0052] Collect historical driving data of two-wheeled vehicles on various quasi-roads, including but not limited to: Speed: The average speed and maximum speed of a two-wheeled vehicle on each section of road.

[0053] Acceleration: The acceleration and deceleration of a two-wheeled vehicle.

[0054] Steering angle: The steering angle of a two-wheeled vehicle when turning.

[0055] Braking conditions: braking frequency and force of a two-wheeled vehicle.

[0056] Driving path: The specific driving path of a two-wheeled vehicle on each section of road.

[0057] Traffic conditions: Traffic conditions encountered by two-wheeled vehicles during driving, such as traffic jams, emergency braking, etc.

[0058] Based on static road data, a basic control chain is constructed. The basic control chain contains a series of basic control instructions, such as maintaining a certain speed on a specific road section, slowing down before an intersection, etc. Define control logic, such as stopping at a red light, starting at a green light, and how to handle emergencies (such as the sudden appearance of pedestrians). The collected two-wheeled vehicle driving data is used as training data, which reflects the actual performance of the two-wheeled vehicle under various road conditions. Select appropriate machine learning models, such as neural networks, decision trees, support vector machines, etc., to train the intelligent control chain. Perform feature engineering on the driving data to extract key features, such as speed, acceleration, steering angle, etc., as input to the model. Use the training data to iteratively train the basic control chain, and use supervised learning methods to continuously adjust the parameters in the control chain so that the control chain can better adapt to the actual driving environment. During the training process, use a part of the data as a validation set to evaluate the performance of the model to ensure that the model is not overfitting. According to the performance evaluation results of the model, optimize and adjust the model to improve the accuracy and robustness of the control chain. Deploy the trained intelligent control chain to the auxiliary control device of the two-wheeled vehicle.

[0059] During the driving process of the two-wheeled vehicle, the auxiliary control device will dynamically adjust the parameters in the intelligent control chain according to the real-time road data and environmental data to cope with different driving conditions. Over time, the system can continue to collect new driving data for further training and optimization of the intelligent control chain to achieve continuous improvement. An intelligent control chain adapted to the specific road environment is constructed to improve the driving safety and efficiency of the two-wheeled vehicle in the second area. This data-driven approach can make the control chain more flexible and intelligent to cope with changing road conditions.

[0060] In some specific embodiments, each first area is pre-configured with one or more main control devices, and each main control device can control one or more two-wheeled vehicles at the same time; when there is one main control device in a first area, the main control device is located at the geometric center of the first area, and the control range is larger than the first area; when there are multiple main control devices in a first area, each main control device is distributed in different areas of the first area according to its own control range, and multiple main control devices are communicated with each other. The main control device can manage multiple two-wheeled vehicles at the same time, coordinate the interaction between them, and avoid collisions and congestion. One main control device controls multiple two-wheeled vehicles as shown in the attached figure. Figure 2As shown. In addition, the signal tower is able to receive data from each two-wheeled vehicle in real time and respond quickly as needed. For example, when a potential security threat is detected, an emergency braking command can be immediately sent to the affected two-wheeled vehicle. In addition, centralized data processing and storage can adopt higher-level security measures such as encryption and firewalls to protect user data from unauthorized access. Placing sensitive data processing in the cloud can better control data access rights and ensure that user privacy is properly protected.

[0061] Whether there is a single or multiple master control devices, the load on the two-wheeled vehicle can be saved. Such a layout can improve the overall reliability of the system. In the case of multiple master control devices, even if a device has a problem, other devices can continue to provide services. When there is only one master control device in the first area, placing it at the geometric center of the area can maximize the coverage and ensure that the signal strength and the transmission of control instructions are as uniform as possible throughout the area. The control range of the master control device should be larger than the actual area of ​​the first area, which means that the two-wheeled vehicle can receive sufficient signal strength and control instructions even at the edge of the area. This additional coverage can provide a certain redundancy to ensure stable control even in the border area. When multiple master control devices need to be deployed in the first area, these devices will be distributed in different sub-areas according to their respective control ranges. This ensures that each sub-area has a master control device in charge, which improves the reliability and response speed of the system. The control ranges between multiple master control devices may overlap, which helps to improve the robustness of the system. Even if a master control device fails or the signal is interfered with, the adjacent master control device can take over the control task of the affected area, thereby reducing the possibility of service interruption. Multiple master control devices are interconnected through high-speed communication networks (such as optical fiber, 5G, etc.). This communication connection enables the various master control devices to share data, coordinate control strategies, and redistribute tasks when necessary. For example, if a two-wheeled vehicle moves from the coverage area of ​​one master control device to the coverage area of ​​another master control device, control can be smoothly transferred. With a distributed layout, resources can be allocated more efficiently. For example, during peak hours, the system can dynamically adjust the load of each master control device as needed to ensure that all two-wheeled vehicles can receive timely service. The master control device can monitor and control the two-wheeled vehicle in real time to ensure that it drives according to predetermined rules and paths. This monitoring and control capability is further enhanced under a multi-device layout. And, as the first area expands or the number of two-wheeled vehicles increases, the system can be easily expanded by adding more master control devices. This flexibility allows the system to adapt to changing needs.

[0062] In some embodiments, it also includes: collecting and analyzing the user's historical riding data to understand the user's common routes and riding habits; based on user preferences and real-time traffic conditions, actively push the best route suggestions. Allow users to book a two-wheeled vehicle through a mobile phone application and set the destination in advance. The system will reserve the vehicle and unlock it when it arrives. Provide personalized travel suggestions and services based on the user's historical data and preferences to improve user satisfaction. In actual applications, necessary sensors (such as GPS, accelerometer, etc.) and on-board units are installed on the two-wheeled vehicle to record riding data such as location, speed, timestamp, riding distance, etc. Collect the user's starting point, end point and other preference information through the mobile phone application or the on-board interface, and obtain real-time traffic data, weather conditions and other external information. Upload the collected data to the cloud database to ensure the safe storage and convenient access of the data. Clean up inaccurate or irrelevant data to ensure data quality. Organize the data according to user ID, time series, etc. for subsequent query and analysis. Identify the user's riding habits, such as common commuting routes, preferred riding time periods, etc. through data mining techniques (such as cluster analysis and sequence pattern mining). Analyze the user's preferences for specific routes, time, weather and other factors. Identify unusual cycling behaviors, such as sudden changes in route or unusual speed changes, which may mean that the user has encountered a problem or changed habits. Recommend similar new routes based on the user's past cycling routes and personal preferences. You can also find other users with similar cycling patterns and recommend routes based on their preferences. Combine content-based recommendations and collaborative filtering, as well as other factors (such as real-time traffic conditions, weather forecasts) to generate more accurate recommendations. By building a system that can understand user needs and provide personalized route recommendations, it can not only improve the user's cycling experience, but also help optimize traffic flow and improve the overall traffic efficiency of the city.

[0063] In some specific embodiments, the first area includes a road portion and a non-road portion, the second area includes only a road portion, and the road portion of the second area is distributed around the first area. Figure 4As shown in Figure 1. The definition of the first and second zones provides multi-level coverage for the management and control of two-wheeled vehicles. The first zone includes not only the road portion but also the non-road portion, which means that it covers a wider geographical range, such as parks, squares, parking lots, etc. The second zone is specifically for the road portion, and some road portions may be scattered around the periphery of the first zone. This zoning approach helps to achieve a more refined management and control strategy. The first zone is designed to provide full coverage, whether the two-wheeled vehicle is traveling on the road or on the non-road (such as through a park or square). This full coverage ensures that no matter where the two-wheeled vehicle is, it can receive the signal from the main control device and be effectively monitored and managed. In addition, the design of the first zone can adapt to more diverse usage scenarios, such as on campus, in residential areas, or in commercial centers. These areas usually have both road and non-road portions, which are suitable for unified management. Since the road and non-road portions included in the first zone may be more complex, the layout of the main control device needs to be more refined. It may be necessary to set up the main control device at some key nodes (such as entrances, exits, and important intersections) to ensure the effectiveness of signal coverage and management. At the same time, good communication connections need to be maintained between the main control devices to support collaborative work. The second zone focuses on the management of road sections, which helps improve road safety and traffic efficiency. This zoning is particularly suitable for areas where higher requirements for traffic flow and safety are required, such as busy city streets or highways. By focusing on road sections, traffic flow can be optimized more effectively, congestion can be reduced, and the traffic efficiency of two-wheeled vehicles can be improved.

[0064] This application proposes an intelligent control system for a two-wheeled vehicle, which systematizes the above-mentioned intelligent control method for a two-wheeled vehicle to make it more practical. Figure 4 The specific plan is as follows: An intelligent control system for a two-wheeled vehicle, comprising: A position detection unit, used for real-time monitoring of two-wheeled vehicles entering a preset area; The first area unit is used to control the main control device to send an intelligent control request to the two-wheeled vehicle when a two-wheeled vehicle enters the first area, and to access the auxiliary control device pre-installed on the two-wheeled vehicle after the request is passed, and control the automatic driving of the two-wheeled vehicle; The sensor device integrated on the two-wheeled vehicle detects environmental data around the two-wheeled vehicle, so that the main control device controls the operation of the two-wheeled vehicle according to the environmental data, thereby realizing automatic driving of the two-wheeled vehicle in the first area; A second area unit is used to obtain a subsequent trip of the two-wheeled vehicle after leaving the current area, and if the subsequent trip involves a preset second area, obtain a pre-configured intelligent control chain for the second area and send it to the auxiliary control device; When the two-wheeled vehicle enters the second area, the auxiliary control device is controlled to execute the intelligent control chain, and the operation of the two-wheeled vehicle is adjusted in combination with the environmental data detected by the sensor device, so as to realize the automatic driving of the two-wheeled vehicle in the second area.

[0065] In some embodiments, it also includes: a travel unit, which is used to determine whether there is pre-input travel data: if so, determine the subsequent travel after leaving the first area based on the travel data; if not, predict the subsequent travel based on the driving path of the two-wheeled vehicle in the first area or the second area.

[0066] The present application also proposes a two-wheeled vehicle, comprising a vehicle body, a main control device, and an auxiliary control device, for implementing any of the above-mentioned intelligent control methods for a two-wheeled vehicle.

[0067] The present application provides a computer program product, which includes computer instructions, which are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes an intelligent control method for a two-wheeled vehicle. An intelligent control method for a two-wheeled vehicle is applied to a computer program product for easy execution.

[0068] The present application also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the intelligent control method for a two-wheeled vehicle as described above are implemented.

[0069] The computer storage medium of the present application may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. The present application applies an intelligent control method for a two-wheeled vehicle to a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the clothing simulation method provided by the present application are implemented, which is simple and fast, easy to store, and not easy to lose.

[0070] Those skilled in the art should understand that the modules of the present application described above can be implemented by a general-purpose computing system, they can be concentrated on a single computing system, or distributed on a network composed of multiple computing systems, and optionally, they can be implemented by program codes executable by a computer system, so that they can be stored in a storage system and executed by the computing system, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0071] Note that the above are only preferred embodiments of the present application and the technical principles used. Those skilled in the art will understand that the present application is not limited to the specific embodiments herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the appended claims.

[0072] The above disclosure only discloses several specific implementation scenarios of the present application. However, the present application is not limited thereto, and any changes that can be conceived by technicians in this field should fall within the scope of protection of the present application.

Claims

1. An intelligent control method for a two-wheeled vehicle, characterized in that: These include: Real-time monitoring of two-wheeled vehicles entering the preset area; When a two-wheeled vehicle enters the preset first area, the preset main control device is controlled to send an intelligent control request to the two-wheeled vehicle, and after the request is passed, the auxiliary control device pre-installed on the two-wheeled vehicle is connected to control the automatic driving of the two-wheeled vehicle; The sensor device integrated on the two-wheeled vehicle detects environmental data around the two-wheeled vehicle, so that the main control device controls the operation of the two-wheeled vehicle according to the environmental data, thereby realizing automatic driving of the two-wheeled vehicle in the first area; Obtaining a subsequent trip of the two-wheeled vehicle after leaving the current area, and if the subsequent trip involves a preset second area, obtaining a pre-configured intelligent control chain for the second area and sending it to the auxiliary control device; When the two-wheeled vehicle enters the second area, the auxiliary control device is controlled to execute the intelligent control chain, and the operation of the two-wheeled vehicle is adjusted in combination with the environmental data detected by the sensor device, so as to realize automatic driving of the two-wheeled vehicle in the second area.

2. The intelligent control method according to claim 1, characterized in that: Also includes: Determine whether there is pre-entered travel data: If so, determining a subsequent trip after leaving the first area according to the trip data; If not present, the subsequent trip is predicted based on the driving path of the two-wheeled vehicle in the first area or the second area.

3. The intelligent control method according to claim 2, characterized in that: Predicting all possible trips of the two-wheeled vehicle after leaving the first area or the second area based on the road map of the first area and the driving path of the two-wheeled vehicle in the first area or the second area, and obtaining a trip prediction result; Continuously adjusting the travel prediction result according to the travel path of the two-wheeled vehicle; After determining the departure area where the two-wheeled vehicle leaves the first area or the second area, one or more first areas and second areas corresponding to the departure area are screened out to obtain a final travel prediction result.

4. The intelligent control method according to claim 1, characterized in that: Also includes: Acquire a road where two-wheeled vehicles are allowed to travel, and divide the road into one or more sub-areas; Determine the population density, traffic flow and infrastructure information in each sub-area and road; Excluding the areas where the population distribution density is greater than a preset density and the areas where the traffic flow is greater than a preset flow in each sub-area, to obtain each candidate area; Analyze the facility perfection of the road according to the infrastructure information, exclude the part of the road where the population distribution density is greater than the preset density, the part of the road where the traffic flow is greater than the preset flow, and the part of the road where the facility perfection is lower than the preset perfection, to obtain multiple discrete candidate roads; All candidate areas and candidate roads that can be connected to each first road are divided into the same area group, and the candidate areas and candidate roads in each area group are integrated to obtain each first area.

5. The intelligent control method according to claim 4, characterized in that: Also includes: taking all candidate roads except those in the first area as quasi-roads; Acquire in advance the road data of each quasi-road and the driving data of the two-wheeled vehicle when driving on each quasi-road; A basic control chain for the auxiliary control device is constructed according to the road data, and the basic control chain is iteratively trained using the driving data as training data to obtain the intelligent control chain.

6. The intelligent control method according to claim 4, characterized in that: Each first area is pre-configured with one or more main control devices, and each main control device can control one or more two-wheeled vehicles at the same time; When there is a main control device in a first area, the main control device is located at the geometric center of the first area, and the control range is larger than the first area; When there are multiple main control devices in a first area, each main control device is distributed in different areas of the first area according to its own control range, and the multiple main control devices are communicatively connected.

7. The intelligent control method according to claim 1, characterized in that: The first area includes a road portion and a non-road portion, the second area includes only a road portion, and part of the road portion of the second area is distributed around the first area.

8. An intelligent control system for a two-wheeled vehicle, characterized in that: These include: A position detection unit, used for real-time monitoring of two-wheeled vehicles entering a preset area; A first area unit is used to control a preset main control device to send an intelligent control request to the two-wheeled vehicle when a two-wheeled vehicle enters the first area, and to access an auxiliary control device pre-installed on the two-wheeled vehicle after the request is passed, and to control the automatic driving of the two-wheeled vehicle; The sensor device integrated on the two-wheeled vehicle detects environmental data around the two-wheeled vehicle, so that the main control device controls the operation of the two-wheeled vehicle according to the environmental data, thereby realizing automatic driving of the two-wheeled vehicle in the first area; A second area unit, used for obtaining a subsequent trip of the two-wheeled vehicle after leaving the current area, and if the subsequent trip involves a preset second area, obtaining a pre-configured intelligent control chain of the second area and sending it to the auxiliary control device; When the two-wheeled vehicle enters the second area, the auxiliary control device is controlled to execute the intelligent control chain, and the operation of the two-wheeled vehicle is adjusted in combination with the environmental data detected by the sensor device, so as to realize automatic driving of the two-wheeled vehicle in the second area.

9. The intelligent control system according to claim 8, characterized in that: Also includes: The travel unit is used to determine whether there is pre-entered travel data: If so, determining a subsequent trip after leaving the first area according to the trip data; If not present, the subsequent trip is predicted based on the driving path of the two-wheeled vehicle in the first area or the second area.

10. A two-wheeled vehicle, characterized in that: It comprises a vehicle body, a main control device and an auxiliary control device, and is used to implement an intelligent control method for a two-wheeled vehicle as described in any one of claims 1-7.

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