Crossing road unmanned vehicle cooperative control method and control system

Abstract

The application relates to a crossroad unmanned vehicle road cooperation control method and a control system. The crossroad unmanned vehicle road cooperation control method can detect road information including road width and traffic light information through an intelligent sensing device arranged at a crossroad, can detect driving information including a lane, a position and a speed of the intelligent automobile, can also detect weather information and information whether the vehicle enters a turning area, acquires actual road conditions through the road on-site sensing device independent of the intelligent automobile, and transmits the information to a cloud computing platform, the cloud computing platform comprehensively judges the road on-site conditions and sends an instruction to the intelligent automobile, the driving state of the intelligent automobile is more accurately controlled, and the safety of straight driving and turning driving of the intelligent automobile at the road crossroad is better ensured.

Description

Technical Field

[0001] This invention relates to the field of traffic control technology for road vehicles, and more particularly to a vehicle-road cooperative control method and control system for unmanned vehicles at intersections. Background Technology

[0002] For ordinary cars, drivers need to constantly monitor the vehicle's turning angle and adjust the steering wheel to control the vehicle's angle and smoothly navigate curves. For large trucks, the large inner wheel difference creates a significant blind spot, making it difficult for drivers to see pedestrians jaywalking while turning, leading to accidents. Weather is also a major factor contributing to loss of control and accidents while turning. Because drivers cannot accurately control their turning speed based on the weather, skidding and loss of control are common in rainy or snowy conditions. Furthermore, while traffic lights play a crucial role in ensuring smooth traffic flow, their lack of flexibility is a significant drawback. Many non-motorized vehicles and pedestrians disregard traffic lights and rely solely on traffic flow to determine whether to cross the road. When cars are rapidly passing through a green light, it is difficult for them to brake in time to stop for pedestrians who suddenly appear, causing chaos at intersections and even serious accidents.

[0003] To address the above issues, numerous domestic and international inventions have focused on inner wheel difference. These studies simulate and fit the actual trajectory of car wheels based on the inner wheel difference, gradually reducing the error in the fitting results caused by the inner wheel difference. However, these methods are difficult to apply in practice because the driver and steering wheel angle are both uncertain factors, leading to significant uncertainty in the inner wheel difference. Furthermore, intersection traffic environments are complex, with numerous vehicles and pedestrians, especially pedestrians, whose movements are highly unpredictable. Current vehicle-based intelligence cannot accurately assess the surrounding environment, and improving its accuracy faces cost and technical challenges. Therefore, relying solely on onboard perception and decision-making at complex intersections makes safe and orderly driving difficult. Summary of the Invention

[0004] The purpose of this invention is to provide a vehicle-road cooperative control method for autonomous vehicles at intersections to solve the safety issues of autonomous vehicles at intersections.

[0005] To achieve the above objectives, the present invention provides a vehicle-road cooperative control method for unmanned vehicles at intersections, the method comprising the following steps:

[0006] S1: The intelligent sensing device detects road information, including road width and traffic light information, driving information of intelligent vehicles approaching intersections, and whether intelligent vehicles have entered the turning area. It then sends the detected information to the cloud computing platform. The detected driving information includes the intelligent vehicle's lane, location, and speed.

[0007] S2: When the distance between the intelligent vehicle and the intersection reaches the predetermined value, the cloud computing platform will send an instruction to the intelligent vehicle to reduce its speed to the predetermined speed.

[0008] S3: When the intelligent vehicle is in a straight lane and the light is green, the intelligent vehicle will drive normally at the predetermined speed.

[0009] S4: When the intelligent vehicle enters the turning area, the cloud computing platform analyzes the steering wheel angle based on the road width, vehicle speed, and turning angle, and sends the instruction to the intelligent vehicle. After receiving the instruction from the cloud computing platform, the intelligent vehicle will make the corresponding turning action.

[0010] S5: When the lane where the intelligent vehicle is located is at a red light, the cloud computing platform will issue a braking command to the intelligent vehicle to wait for passage. When the lane where the intelligent vehicle is located is at a green light, the cloud computing platform will issue a forward command to the intelligent vehicle, and then repeat S3-S4.

[0011] The vehicle-road cooperative control method for unmanned vehicles at intersections of this invention utilizes intelligent sensing devices installed at intersections to detect road information, including road width and traffic light information, driving information, including the lane, position, and speed of the intelligent vehicle, as well as weather information and whether the vehicle has entered a turning area. By acquiring the actual road conditions through road-side sensing devices independent of the intelligent vehicle, this information is transmitted to a cloud computing platform. The cloud computing platform then comprehensively judges these road conditions and sends instructions to the intelligent vehicle, achieving more accurate control over the vehicle's driving status and thus better ensuring the safety of the intelligent vehicle's straight-line driving and turning at intersections. Furthermore, since the intelligent sensing devices provide data support for the intelligent vehicle on the road, higher-quality autonomous driving can be achieved using the existing hardware configuration of lower-level autonomous vehicles. This reduces the level of autonomous driving and development costs of intelligent vehicles, improves factory order and efficiency, and also reduces driver wages, facilitating widespread adoption and enhancing practicality.

[0012] Furthermore, within the turning area, cameras mounted above the rear wheels on both sides of the intelligent vehicle detect the distance between the rear wheels and the lane lines. The intelligent vehicle sends this real-time distance data to the cloud computing platform. Based on the received real-time data from the turning area, the cloud computing platform controls the vehicle's steering angle in real time to prevent the vehicle from leaving its lane during a turn. By using cameras mounted above the rear wheels on both sides of the intelligent vehicle, the distance between the rear wheels and the lane lines can be determined more accurately. Transmitting this distance information to the cloud computing platform allows for real-time and appropriate control of the vehicle's steering angle, preventing the vehicle from deviating from its lane due to inner wheel differences during the turning process.

[0013] Furthermore, within the turning area, the intelligent sensing device detects the deflection angle between the vehicle body and the lane lines, and sends this real-time data to the cloud computing platform. The cloud computing platform then uses this data to control the vehicle's steering angle in real time, preventing the vehicle from leaving its lane during a turn. By detecting the deflection angle between the vehicle body and the lane lines and transmitting this information to the cloud computing platform in real time, the platform can effectively control the vehicle's steering angle, preventing the car from deviating from its lane due to inner wheel differences during the turning process.

[0014] Furthermore, within the turning area, cameras mounted above the rear wheels on both sides of the intelligent vehicle detect the distance between the rear wheels and the lane lines. The intelligent vehicle sends this real-time distance data to the cloud computing platform. Intelligent sensing devices detect the deflection angle between the vehicle body and the lane lines and send this real-time deflection angle data to the cloud computing platform. Based on the received deflection angle and distance data between the rear wheels and the lane lines, the cloud computing platform controls the intelligent vehicle's steering angle in real-time to prevent it from leaving its lane during a turn. Simultaneously detecting the distance between the rear wheels and the lane lines, as well as the deflection angle, and sending this real-time data to the cloud computing platform provides more comprehensive and accurate data support. This facilitates the cloud computing platform's real-time and rational control of the intelligent vehicle's steering angle, preventing the vehicle from deviating from its lane due to inner wheel differences during the turning process.

[0015] Furthermore, the pedestrian detection equipment monitors the zebra crossing area in real time to see if any pedestrians are entering. When the distance between the intelligent vehicle and the intersection is within a predetermined value, and a pedestrian is detected entering the zebra crossing area, a braking command is sent to the intelligent vehicle, causing it to brake and stop. This enables the intelligent vehicle to yield to pedestrians more reliably, ensuring the safe passage of pedestrians.

[0016] Furthermore, the intelligent sensing device is also used to detect weather information. In the turning area, the intelligent sensing device also detects the deflection angle between the vehicle body and the lane line. The intelligent sensing device sends the weather information, deflection angle and vehicle speed information to the cloud computing platform. The cloud computing platform controls the vehicle speed reasonably based on the weather conditions collected by the intelligent sensing device and calculates the optimal front wheel steering angle.

[0017] Meanwhile, the present invention also provides a vehicle-road cooperative control system for unmanned vehicles at intersections to solve the safety issues of unmanned vehicles at intersections.

[0018] The system mainly includes intelligent sensing devices, a cloud computing platform, and in-vehicle intelligent devices. The intelligent sensing devices are deployed on infrastructure at road intersections to detect road information such as road width and traffic light information, driving information of intelligent vehicles approaching the intersection, and whether intelligent vehicles have entered the turning zone. The driving information of intelligent vehicles includes lane, position, and speed information. The driving status information of intelligent vehicles entering the turning zone includes vehicle yaw angle and speed information. This information is transmitted wirelessly to the cloud computing platform. The cloud computing platform receives the detected intelligent vehicle information from the intelligent sensing devices and in-vehicle intelligent devices, and issues the following decision instructions to the in-vehicle intelligent devices based on the situation: when the distance between the intelligent vehicle and the intersection reaches a predetermined value, the cloud computing platform... The platform sends instructions to the in-vehicle intelligent devices to reduce the vehicle's speed to a predetermined speed. When the intelligent vehicle is in a straight lane with a green light, the in-vehicle intelligent devices control the vehicle to drive normally at the predetermined speed. When the intelligent vehicle enters a turning area, the cloud computing platform analyzes the steering wheel angle based on the road width, vehicle speed, and curve angle, and sends instructions to the in-vehicle intelligent devices to control the vehicle to make corresponding turning maneuvers according to the instructions received from the cloud computing platform. When the intelligent vehicle is in a red light, the cloud computing platform sends braking instructions to the in-vehicle intelligent devices to wait for passage. When the intelligent vehicle is in a green light, the cloud computing platform sends forward instructions to the in-vehicle intelligent devices to control the vehicle to make corresponding actions according to the lane.

[0019] The vehicle-road cooperative control system for unmanned vehicles at intersections of this invention provides intelligent vehicles with a broad field of vision. By adding intelligent sensing devices to the infrastructure at road intersections, it acquires road information, driving information of intelligent vehicles approaching the intersection, and information on whether the intelligent vehicles have entered the turning zone. This information is then sent to a cloud computing platform, enabling the platform to make decisions and control the vehicle's steering. By acquiring real-time data, the cloud computing platform comprehensively judges and sends instructions to the intelligent vehicles, achieving more accurate control over their driving status and thus better ensuring the safety of intelligent vehicles driving straight and turning at intersections. Moreover, since the intelligent sensing devices provide data support for intelligent vehicles on the road, higher-quality autonomous driving can be achieved using the existing hardware configuration of lower-level autonomous vehicles. This reduces the level of autonomous driving and development costs of intelligent vehicles, improves factory order and efficiency, and also reduces driver wages, facilitating widespread adoption and enhancing practicality.

[0020] Furthermore, the intelligent sensing device includes an information collection device, an information processing device, a wireless information communication device, and a power supply device. The information collection device is electrically connected to the information processing device, and the information processing device is electrically connected to the wireless information communication device. The power supply device provides power to the above three devices. The information collection device includes one or more of a camera, LiDAR, and millimeter-wave radar, used to detect vehicle information. The wireless information communication device can use a device based on RFID or LoRa technology to achieve wireless data transmission. The information processing device can be implemented using a multi-core heterogeneous SOC + MCU platform solution. All modules of the intelligent sensing device can be implemented using existing mature equipment and technologies, reducing manufacturing costs and ensuring the stability of device operation.

[0021] Furthermore, the in-vehicle intelligent device includes a data receiving device, an information processing device, a wireless data communication device, and a vehicle controller. The data receiving device is electrically connected to both the wireless data communication device and the vehicle controller. The wireless data communication device is wirelessly connected to the cloud computing platform to receive commands and send data. This enables better communication and control between the intelligent sensing device, the cloud computing platform, and the intelligent vehicle, ensuring the reliability of the system operation.

[0022] Furthermore, the in-vehicle intelligent device also includes cameras mounted above the rear wheels on both sides of the vehicle. The cameras are electrically connected to the information processing device. During the turning process of the intelligent vehicle, the cameras mounted above the rear wheels on both sides of the intelligent vehicle will detect the distance between the rear wheels and the lane lines and send the real-time data information to the cloud computing platform for real-time feedback via wireless communication. This allows the cloud computing platform to perform real-time and reasonable control of the steering angle of the intelligent vehicle based on the real-time data information and the information detected by the intelligent sensing device, which includes at least the deflection angle between the vehicle body and the lane lines and the vehicle speed information, so as to prevent the vehicle from deviating from the original lane due to the inner wheel difference during the turning process.

[0023] Furthermore, the cloud computing platform includes a data receiving module, a data processing module, an analysis and decision-making module, a wireless communication module, and a power supply module. The data receiving module is electrically connected to the data processing module, the data processing module is electrically connected to the analysis and decision-making module, the analysis and decision-making module is electrically connected to the wireless communication module, and the power supply module provides power to all the above modules. The analysis and decision-making module calculates the optimal front wheel steering angle and vehicle speed of the intelligent vehicle based on the database built by the data processing module.

[0024] Furthermore, the control system also includes pedestrian detection equipment, which comprises a pedestrian information detection device, a pedestrian information analysis and processing device, a pedestrian information wireless communication device, and a power supply device. The pedestrian information detection device is electrically connected to the pedestrian information analysis and processing device, which is also electrically connected to the pedestrian information wireless communication device. The power supply device provides power to all these devices. The pedestrian information wireless communication device is wirelessly connected to the intelligent vehicle. The pedestrian information detection device detects external information and sends it to the pedestrian information analysis and processing device to determine if a pedestrian has entered the crosswalk area. When the distance between the intelligent vehicle and the intersection is within a predetermined value, and a pedestrian is detected entering the crosswalk area, a braking command is generated and sent to the onboard intelligent device via the wireless communication device. This enables the intelligent vehicle to yield to pedestrians more reliably, ensuring the safe passage of pedestrians.

[0025] Furthermore, the decision-making instructions from the cloud computing platform and the braking instructions detected by the pedestrian detection equipment when a pedestrian enters the zebra crossing area are transmitted wirelessly to the onboard intelligent device. The data receiving device then sends these two instructions to the vehicle controller, which in turn controls the intelligent vehicle to perform the corresponding functions. The onboard intelligent device prioritizes executing the instructions from the pedestrian detection equipment. By controlling the intelligent vehicle through the vehicle controller, the vehicle's driving status can be conveniently and accurately controlled without requiring any modifications to the vehicle. Moreover, this prioritizes pedestrian safety, thus improving driving safety.

[0026] Furthermore, the information collection device of the intelligent sensing equipment also includes a weather sensor. The cloud computing platform controls the vehicle speed reasonably based on the weather conditions collected by the intelligent sensing equipment. At the same time, it will also detect the offset angle between the vehicle body and the lane line and send it to the cloud computing platform so that the cloud computing platform can calculate the optimal front wheel steering angle based on the vehicle speed and the offset angle. Attached Figure Description

[0027] Figure 1 This is a schematic diagram illustrating the principle of the cross-road autonomous vehicle cooperative control system of the present invention.

[0028] Figure 2 This is a control flowchart of the cross-road autonomous vehicle cooperative control system of the present invention;

[0029] Figure 3 This is a flowchart of the control logic of the cross-road autonomous vehicle cooperative control system of the present invention.

[0030] In the diagram: 1. Lane; 10. Zebra crossing; 20. Cloud computing platform; 21. Pedestrian detection equipment; 22. Intelligent sensing equipment; 23. Intelligent vehicle. Detailed Implementation

[0031] The features and performance of the present invention will be further described in detail below with reference to embodiments.

[0032] The vehicle-road cooperative control system for unmanned vehicles at intersections of the present invention is more suitable for application at urban road intersections. It obtains the actual road conditions, traffic conditions, pedestrian flow, and weather conditions at the road intersection through an external information detection device set up at the intersection, and generates control commands for the intelligent vehicle 23 to control its driving status accordingly based on this information. In this way, the intelligent vehicle 23’s own control and external control are combined, which can accurately and reasonably control the driving status of the intelligent vehicle 23 and ensure the safety of the intelligent vehicle 23 when driving straight and turning at road intersections.

[0033] Specifically, such as Figure 1-3 As shown, this embodiment provides a vehicle-road cooperative control system for unmanned vehicles at intersections, mainly including intelligent sensing devices 22, pedestrian detection devices 21, onboard intelligent devices installed on intelligent vehicles 23, and a cloud computing platform 20. The intelligent sensing devices 22 are deployed on infrastructure at road intersections, such as on traffic light fixtures or on specially installed vertical supports at the intersection, to detect information such as the intelligent vehicle 23's lane position, speed, traffic lights, and weather conditions, and transmit this information wirelessly to the cloud computing platform 20 (e.g., ...). Figure 1As shown, the intelligent sensing device 22 is installed on the light fixture of the traffic signal light, and is located in the middle of the two-way lane, evenly distributed. (In one embodiment, it can also be installed on the guardrails on both sides of the lane). The cloud computing platform 20 performs comprehensive judgment and processing based on the real-time data sent by the intelligent sensing device 22, and then sends corresponding instructions to the on-board intelligent device, controlling the intelligent vehicle 23 to make corresponding changes in its driving status; installed near the zebra crossing 10 (e.g., Figure 1 As shown, at the end of zebra crossing 10, for intersections with corners, a pedestrian detection device (preferably placed at the intersection of two adjacent zebra crossings 10) can detect pedestrian movements. When the intelligent vehicle 23 enters the predetermined range of the pedestrian detection device, it will slow down to an appropriate speed. When the pedestrian detection device detects a pedestrian entering the zebra crossing 10 area, it will generate a braking command and send the command to the intelligent vehicle 23 to make it brake and stop to yield to the pedestrian, ensuring the pedestrian's safe passage.

[0034] The intelligent sensing device 22 includes an information collection device, an information processing device, a wireless information communication device, and a power supply device. The information collection device is electrically connected to the information processing device, and the information processing device is electrically connected to the wireless information communication device. The power supply device provides power to the above three devices and can be powered by solar panels in conjunction with the transportation power grid.

[0035] The information collection device includes a camera, lidar, millimeter-wave radar, and weather sensors. The camera, lidar, millimeter-wave radar, and weather sensors are all electrically connected to the information processing device and send the collected external information to it. The information collection device detects vehicle and weather information so that the cloud computing platform 20 can appropriately control the vehicle speed based on weather conditions. It also detects the vehicle's deviation angle from the lane line and sends it to the cloud computing platform 20, allowing the platform to calculate the optimal front wheel steering angle based on the vehicle speed and deviation angle.

[0036] Both lidar and millimeter-wave radar can detect features such as the position and speed of the intelligent vehicle 23. Millimeter-wave radar has moderate recognition capabilities but strong penetration, and is less affected by weather conditions. LiDAR, on the other hand, has high accuracy but poor penetration and is susceptible to fog, rain, and snow. Therefore, combining lidar and millimeter-wave radar, with millimeter-wave radar taking the lead in inclement weather and lidar taking the lead in good weather, and both working in conjunction with meteorological sensors to detect vehicle and weather information, significantly increases detection accuracy. This allows the cloud computing platform 20 to rationally control the vehicle speed based on weather conditions. Alternatively, in other embodiments, a camera and lidar, or a camera and millimeter-wave radar, can be used together to detect the intelligent vehicle's position and speed.

[0037] The wireless information communication device of the intelligent sensing device 22 can be a device that realizes wireless data transmission based on RFID or LoRa technology. For example, a wireless data transmission device of model USR-LG206-LC can be used. The information processing device can be implemented by a multi-core heterogeneous SOC+MCU platform solution processing device, such as a processor of model TC275.

[0038] The pedestrian detection device 21 includes a pedestrian information detection unit, a pedestrian information analysis and processing unit, a pedestrian information wireless communication unit, and a power supply unit. The pedestrian information detection unit includes a camera and a lidar, both of which are electrically connected to the pedestrian information analysis and processing unit. The pedestrian information analysis and processing unit is also electrically connected to the pedestrian information wireless communication unit. The power supply unit provides power to all the above devices and can be powered by solar panels in conjunction with the traffic power grid. The pedestrian information wireless communication unit is wirelessly connected to the on-board intelligent equipment of the intelligent vehicle 23. The camera, in conjunction with the lidar, detects pedestrian information and sends the information to the pedestrian information analysis and processing unit to determine whether a pedestrian is about to cross the road. If a pedestrian is detected entering the zebra crossing 10 area, a braking command is generated and sent to the on-board intelligent equipment via the pedestrian information wireless communication unit, causing the intelligent vehicle 23 to brake and stop to yield to the pedestrian. Of course, in other embodiments, the pedestrian information detection unit can also use millimeter-wave radar and lidar in combination, or a camera and millimeter-wave radar in combination.

[0039] The in-vehicle intelligent equipment specifically includes a data receiving device, an information processing device, a wireless data communication device, and a vehicle controller. The data receiving device is wirelessly connected to the cloud computing platform 20 and the pedestrian detection device 21. The data receiving device is electrically connected to the vehicle controller. The vehicle controller is electrically connected to the drive control device, braking control device, and steering control device of the intelligent vehicle 23. The camera is electrically connected to the information processing device, and the information processing device is electrically connected to the wireless data communication device.

[0040] The data receiving device is used to receive instructions from the cloud computing platform 20 and the pedestrian detection device 21, and sends the instruction data to the vehicle controller via electrical connection. The vehicle controller controls the drive control device, braking control device and steering control device to perform corresponding actions according to the instruction data, so that the car can perform forward, reverse, stop and turn. When abnormal situations occur, it can also realize the functions of deceleration, stopping and avoidance of the intelligent car 23.

[0041] The in-vehicle intelligent device also includes cameras mounted above the rear wheels on both sides of the vehicle. The cameras are electrically connected to the information processing device. During the turning process of the intelligent vehicle 23, the cameras mounted above the rear wheels on both sides of the intelligent vehicle 23 will detect the distance between the rear wheels and the lane lines and send the information to the information processing device via electrical connection. The information processing device will calculate the distance between the rear wheels and the lane lines and send it to the cloud computing platform 20 for real-time feedback via wireless communication. This allows the cloud computing platform 20 to perform real-time and reasonable control of the steering angle of the intelligent vehicle 23, which can prevent the vehicle from deviating from its original lane due to the inner wheel difference during the turning process.

[0042] The cloud computing platform 20 includes a data receiving module, a data processing module, an analysis and decision-making module, a wireless communication module, and a power supply module. The data receiving module is electrically connected to the data processing module, the data processing module is electrically connected to the analysis and decision-making module, and the analysis and decision-making module is electrically connected to the wireless communication module. The power supply module provides power to all the above modules and can use solar panels in conjunction with the transportation power grid for power supply.

[0043] The data receiving module receives information from the intelligent sensing device 22, including lane information, location, speed, traffic light status, and weather conditions of the intelligent vehicle 23. It also receives information from the intelligent vehicle 23 regarding the distance between its rear wheels and the lane lines. This information is then sent to the data processing module. The data processing module performs preliminary processing on the information received from the data receiving module and builds a database. This database is then sent to the analysis and decision-making module. Based on the database built by the data processing module, the analysis and decision-making module calculates the optimal front wheel steering angle and speed of the intelligent vehicle 23. It then uses the distance between the rear wheels and the lane lines provided by the intelligent vehicle 23 to control the steering angle and speed of the intelligent vehicle 23 in real time. This prevents the vehicle from deviating from its lane due to the inner wheel difference during steering. In other words, the cloud computing platform 20 will determine the reasonable speed of the intelligent vehicle 23 based on its location and weather conditions. It will also calculate the optimal front wheel steering angle of the intelligent vehicle 23 based on the vehicle speed and the deflection angle between the vehicle body and the lane line detected by the intelligent sensing device 22. At the same time, during the steering process of the intelligent vehicle 23, the cameras installed above the rear wheels on both sides of the intelligent vehicle 23 will detect the distance between the rear wheels and the lane line and send the information to the cloud computing platform 20 for real-time feedback via wireless communication, so that the cloud computing platform 20 can perform real-time and reasonable control of the steering angle of the intelligent vehicle 23.

[0044] Similarly, the information collected by the intelligent sensing device 22 can also be transmitted in real time to the cloud computing platform 20 via a wireless communication device that transmits data through fiber optic or 4G / 5G wireless signals. The intelligent sensing device 22 not only has short-term real-time data collection capabilities but also has the long-term expansion capability to connect with fiber optic or 4G / 5G wireless signal transmission systems.

[0045] This invention enables intelligent vehicles to turn intelligently at intersections, thereby achieving autonomous driving capabilities ahead of time for current and future mass-produced L2 / L3 level intelligent vehicles, with minimal increases in vehicle costs. This allows urban transportation to enter the era of autonomous driving sooner. Simultaneously, the cloud computing platform can wirelessly transmit data to higher-level autonomous vehicles, enabling their advanced driver assistance functions. Therefore, this system also holds promise for future applications in more advanced autonomous vehicles.

[0046] Based on the above-described vehicle-to-infrastructure (V2I) cooperative control system for autonomous vehicles on intersecting roads, this paper also provides a V2I cooperative control method for autonomous vehicles on intersecting roads, as detailed below:

[0047] S1: The intelligent sensing device detects road information, including road width and traffic light information, driving information of intelligent vehicles approaching intersections, and whether intelligent vehicles have entered the turning area. It then sends the detected information to the cloud computing platform. The detected driving information includes the intelligent vehicle's lane, location, and speed.

[0048] S2: When the distance between the intelligent vehicle and the intersection reaches the predetermined value, the cloud computing platform will send a command to the intelligent vehicle 23 to reduce the speed of the intelligent vehicle to the predetermined speed.

[0049] S3: When the intelligent vehicle is in a straight lane and the light is green, the intelligent vehicle will drive normally at the predetermined speed.

[0050] S4: When the intelligent vehicle enters the turning area, the cloud computing platform analyzes the road width, vehicle speed and turning angle to determine the optimal steering angle of the steering wheels and sends the instruction to the intelligent vehicle. After receiving the instruction from the cloud computing platform, the intelligent vehicle will make the corresponding turning action.

[0051] S5: When the lane where the intelligent vehicle is located is at a red light, the cloud computing platform will issue a braking command to the intelligent vehicle to wait for passage. When the lane where the intelligent vehicle is located is at a green light, the cloud computing platform will issue a forward command to the intelligent vehicle, and then repeat S3-S4.

[0052] With the help of a vehicle-to-infrastructure (V2I) cooperative control system for autonomous vehicles at intersections, and building upon the above methods, a more optimized design allows for the intelligent vehicle to stop when the distance to the intersection is within a predetermined value and the pedestrian detection device detects a pedestrian entering the crosswalk area. If the pedestrian detection device does not detect a pedestrian entering the crosswalk area, no braking command is issued, and the intelligent vehicle executes the action commands from the cloud computing platform. Commands sent to the intelligent vehicle by the pedestrian detection device have higher priority, ensuring that the intelligent vehicle reliably yields to pedestrians and improving driving safety.

[0053] As another optimized design, within the turning area, cameras mounted above the rear wheels on both sides of the intelligent vehicle detect the distance between the rear wheels and the lane lines. The intelligent vehicle sends the detected distance data in real time to a cloud computing platform. The cloud computing platform then controls the steering angle of the intelligent vehicle in real time based on the received real-time data from the turning area, preventing the intelligent vehicle from leaving its original lane while turning. For the same purpose, in some implementations, simultaneously or alternatively, within the turning area, intelligent sensing devices can detect the deflection angle between the vehicle body and the lane lines and send the detected deflection angle data in real time to a cloud computing platform. The cloud computing platform then controls the steering angle of the intelligent vehicle in real time based on the received real-time data from the turning area, preventing the intelligent vehicle from leaving its original lane while turning.

[0054] In addition, while preferred, but not mandatory, the intelligent sensing device is also used to detect weather information. Within the turning area, the intelligent sensing device also detects the yaw angle between the vehicle body and the lane lines. The intelligent sensing device sends the weather information, yaw angle, and vehicle speed information to the cloud computing platform. The cloud computing platform then uses the weather conditions collected by the intelligent sensing device to reasonably control the vehicle speed and calculate the optimal front wheel steering angle. This combination of weather information and control of the turning process improves the accuracy of intelligent vehicle control.

[0055] As can be seen from the above introduction of the cross-road unmanned vehicle cooperative control system and control method of the present invention, the present invention establishes control communication with the intelligent vehicle through intelligent sensing devices, cloud computing platforms and pedestrian detection devices independent of the intelligent vehicle itself. It can combine more accurate and comprehensive external control based on real-time road conditions on the basis of the intelligent vehicle's own control, thereby more accurately controlling the driving status of the intelligent vehicle and improving the intelligence and driving safety of the intelligent vehicle.

[0056] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. The scope of patent protection of the present invention shall be determined by the claims. Similarly, any equivalent structural changes made based on the description and drawings of the present invention shall also be included within the scope of protection of the present invention.

Claims

1. A vehicle-road cooperative control method for unmanned vehicles on intersecting roads, characterized in that, The method includes the following steps: S1: The intelligent sensing device detects information including road width and traffic lights, driving information of intelligent vehicles approaching intersections, and whether intelligent vehicles have entered the turning area. It then sends the detected information to the cloud computing platform. The detected driving information includes the intelligent vehicle's lane, location, and speed. S2: When the distance between the intelligent vehicle and the intersection reaches the predetermined value, the cloud computing platform will send an instruction to the intelligent vehicle to reduce its speed to the predetermined speed. S3: When the intelligent vehicle is in a straight lane and the light is green, the intelligent vehicle will drive normally at the predetermined speed. S4: When the intelligent vehicle enters the turning area, the cloud computing platform analyzes the steering wheel angle based on the road width, vehicle speed and turning angle, and sends the instruction to the intelligent vehicle. After receiving the instruction from the cloud computing platform, the intelligent vehicle will make the corresponding turning action. S5: When the lane where the intelligent vehicle is located is at a red light, the cloud computing platform will issue a braking command to the intelligent vehicle to wait for passage. When the lane where the intelligent vehicle is located is at a green light, the cloud computing platform will issue a forward command to the intelligent vehicle, and then repeat S3-S4. Within the turning area, cameras mounted above the rear wheels on both sides of the intelligent vehicle detect the distance between the rear wheels and the lane lines. The intelligent vehicle sends the detected distance data to the cloud computing platform in real time. The intelligent sensing device detects the deflection angle between the vehicle body and the lane lines and sends the detected deflection angle data to the cloud computing platform in real time. The cloud computing platform controls the steering angle of the intelligent vehicle in real time based on the received deflection angle and the real-time distance data between the rear wheels and the lane lines to prevent the intelligent vehicle from leaving the original lane when turning.

2. The control method according to claim 1, characterized in that: The pedestrian detection equipment monitors in real time whether pedestrians are entering the zebra crossing area. When the distance between the intelligent vehicle and the intersection is within a predetermined value and a pedestrian is detected entering the zebra crossing area, a braking command is sent to the intelligent vehicle to make it brake and stop.

3. The control method according to claim 1, characterized in that: The intelligent sensing device is also used to detect weather information. In the turning area, the intelligent sensing device sends the weather information, yaw angle and vehicle speed information to the cloud computing platform. The cloud computing platform controls the vehicle speed reasonably and calculates the optimal front wheel steering angle based on the weather conditions collected by the intelligent sensing device.

4. A vehicle-road cooperative control system for unmanned vehicles at intersections, the system comprising intelligent sensing devices, a cloud computing platform, and onboard intelligent devices, characterized in that: The intelligent sensing devices are deployed on infrastructure at road intersections to detect road width and traffic light information, the driving information of intelligent vehicles approaching the intersection, and whether intelligent vehicles have entered the turning area. The driving information of intelligent vehicles includes lane, position, and speed. The driving status information of intelligent vehicles entering the turning area includes vehicle yaw angle and speed. This information is transmitted wirelessly to a cloud computing platform. The cloud computing platform receives the detected intelligent vehicle information from the intelligent sensing devices and the onboard intelligent devices, and issues the following decision instructions to the onboard intelligent devices as needed: When the distance between the intelligent vehicle and the intersection reaches a predetermined value, the cloud computing platform sends an instruction to the onboard intelligent devices to reduce the intelligent vehicle's speed to a predetermined speed; when the intelligent vehicle's lane is a straight lane and the light is green, the onboard intelligent devices control the intelligent vehicle to drive normally at the predetermined speed; when the intelligent vehicle enters the turning area, the cloud computing platform analyzes the steering wheel angle based on the road width, vehicle speed, and turning angle, and sends an instruction to the vehicle... The system incorporates intelligent devices that control the intelligent vehicle to perform corresponding turning maneuvers according to instructions sent from a cloud computing platform. When the intelligent vehicle's lane is at a red light, the cloud computing platform issues a braking command to the onboard intelligent devices to wait for passage. When the intelligent vehicle's lane is at a green light, the cloud computing platform issues a forward command to the onboard intelligent devices, controlling the intelligent vehicle to perform corresponding actions according to the lane. The onboard intelligent devices include cameras mounted above the rear wheels on both sides of the vehicle. The cameras are electrically connected to an information processing device. During the intelligent vehicle's turning process, the cameras mounted above the rear wheels on both sides detect the distance between the rear wheels and the lane lines and transmit this real-time data information to the cloud computing platform via wireless communication for real-time feedback. This allows the cloud computing platform to perform real-time and reasonable control of the intelligent vehicle's steering angle based on this real-time data information and information detected by the intelligent sensing devices, which includes at least the deflection angle between the vehicle body and the lane lines and the vehicle speed, to prevent the vehicle from deviating from its original lane due to the inner wheel difference during the turning process.

5. The control system according to claim 4, characterized in that: The intelligent sensing device includes an information collection device, an information processing device, a wireless information communication device, and a power supply device. The information collection device is electrically connected to the information processing device, and the information processing device is electrically connected to the wireless information communication device. The power supply device provides power to the above three devices. The information collection device includes one or more of a camera, lidar, and millimeter-wave radar, used to detect vehicle information. The wireless information communication device uses RFID or LoRa technology to achieve wireless data transmission. The information processing device is implemented using a platform solution of multi-core heterogeneous SOC + MCU.

6. The control system according to claim 4, characterized in that: The in-vehicle intelligent device includes a data receiving device, an information processing device, a wireless data communication device, and a vehicle controller. The data receiving device is electrically connected to both the wireless data communication device and the vehicle controller. The wireless data communication device is wirelessly connected to a cloud computing platform to receive instructions and send data information.

7. The control system according to any one of claims 4-6, characterized in that: The control system also includes pedestrian detection equipment, which comprises a pedestrian information detection device, a pedestrian information analysis and processing device, a pedestrian information wireless communication device, and a power supply device. The pedestrian information detection device is electrically connected to the pedestrian information analysis and processing device, and the pedestrian information analysis and processing device is electrically connected to the pedestrian information wireless communication device. The power supply device provides power to all the above devices. The pedestrian information wireless communication device is wirelessly connected to the intelligent vehicle. The pedestrian information detection device detects external information and sends the information to the pedestrian information analysis and processing device to determine whether a pedestrian has entered the zebra crossing area. When the distance between the intelligent vehicle and the intersection is within a predetermined value and a pedestrian is detected entering the zebra crossing area, a braking command is generated and sent to the on-board intelligent device through the wireless communication device.

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