An intelligent network connected automobile automatic driving detection control system
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANDONG SINO-AISA TIRE PROVING GROUND CO LTD
- Filing Date
- 2026-03-16
- Publication Date
- 2026-06-19
Smart Images
Figure CN122232666A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the automotive field, specifically to an intelligent connected vehicle autonomous driving detection and control system. Background Technology
[0002] Intelligent connected vehicles are a deep integration of vehicle networking and intelligent vehicles, aiming to gradually replace manual driving in the next generation of automobiles through highly intelligent technologies. They integrate advanced onboard sensors, controllers, actuators, and other devices, and combine modern communication and network technologies to achieve comprehensive information interaction and sharing between vehicles, roads, people, and cloud platforms.
[0003] As the core of intelligent connected vehicles, autonomous driving technology directly determines the upper limit of the entire industry's development through the accuracy, reliability, and adaptability of its environmental perception and decision-making control. Currently, autonomous driving systems heavily rely on sensors such as vision cameras, millimeter-wave radar, and lidar mounted on the vehicle to perceive the surrounding environment, identify the relative position, speed, and outline of the vehicle in front, and thereby realize functions such as adaptive cruise control and emergency braking.
[0004] However, the effectiveness of sensors is highly correlated with weather and lighting conditions. In adverse conditions such as rain, snow, fog, and backlighting, sensing performance drops sharply or even fails, creating a "perception black hole" and leading to control failure. Sensors can only acquire the physical outline and motion state of a target, but cannot identify its intrinsic attributes (such as the specific vehicle model, weight, and power performance). Therefore, when faced with different vehicle types, they cannot make differentiated, physically-based following or avoidance decisions. For example, if a passenger car follows a heavy truck at the same distance, the braking requirements will be drastically different, posing a safety hazard.
[0005] Current systems primarily focus on frontal environmental perception, with relatively limited monitoring capabilities for vehicles to the sides and rear. Assessing the status of vehicles approaching from behind relies mainly on visual and radar data. In extreme weather conditions, the accuracy of radar and cameras can be affected, leading to recognition delays or malfunctions, making it difficult for the system to accurately assess the risks of overtaking and lane-changing maneuvers. Furthermore, the system's avoidance strategies are typically fixed, lacking the ability to quickly identify safe driving areas and dynamically plan paths. Its proactive response and collaborative capabilities in complex scenarios still need improvement, which to some extent limits the safety and reliability of autonomous driving.
[0006] Based on this, this application proposes an intelligent connected vehicle autonomous driving detection and control system. Summary of the Invention
[0007] This application proposes an intelligent connected vehicle autonomous driving detection and control system, which has the following advantages: through weather adaptive control strategy, same model imitation trajectory and on-vehicle verification logic, rear overtaking defense mechanism and multi-source data fusion technology, it realizes high-precision, high-safety and high-efficiency driving of autonomous driving in complex weather and multi-vehicle interaction environments, and solves the technical problems mentioned in the background.
[0008] To achieve the above objectives, this application adopts the following technical solution: an intelligent connected vehicle autonomous driving detection and control system, including an environmental perception module, a data acquisition module, a decision planning module, a vehicle control module, and a communication module, wherein the environmental perception module is used to acquire and determine current weather information and generate a first mode signal or a second mode signal; The data acquisition module is used to collect data from vehicles in front and behind, vehicle operation data, roadside edge device unit data, and map information. It also determines the priority of different types of data collection based on weather type, and collects and uploads data according to the priority. When the decision-making and planning module receives the first mode signal, it prioritizes receiving data transmitted from the edge unit, combines it with the information of the vehicles in front and behind collected by the data acquisition module, determines the distance between the vehicle and the vehicles in front and behind, adjusts the vehicle's speed, and determines a safe driving area. When the vehicle in front is the same model as the vehicle in front, the decision-making and planning module controls the vehicle to imitate the trajectory of the vehicle in front while ensuring safe driving. When the vehicle in front is different from the vehicle in front, the decision-making and planning module dynamically adjusts the vehicle's safe distance according to the model difference and makes independent decisions based on the vehicle's sensors. When the decision-making and planning module receives the second mode signal, the decision... The planning module prioritizes receiving historical data of vehicles ahead and behind from the roadside edge device unit, map information, information of vehicles ahead and behind received via broadcast, and the vehicle's own operating data. Combining the map information and the information from the data uploaded by vehicles ahead and behind to the roadside edge device unit, it predicts the positions of vehicles ahead and behind and their distances from the vehicle, determines the safe driving area, and adjusts the vehicle speed based on the information of vehicles ahead and behind received via broadcast, generating corresponding driving control commands. Furthermore, when a vehicle behind overtakes, the decision planning module comprehensively identifies and analyzes the safe driving area, formulates a new driving path within the safe driving area, and generates corresponding control commands.
[0009] Preferably, the communication module is used to realize data interaction between the vehicle and the edge unit, and data interaction between the vehicle and the vehicles in front and behind, and the vehicle and the vehicles in front and behind realize data interaction through broadcast.
[0010] Preferably, the vehicle receives basic safety information from the preceding and following vehicles via broadcast, including vehicle size, dynamic status, and vehicle type.
[0011] Preferably, the environmental perception module is used to distinguish between sunny weather and rainy, snowy, or foggy weather, and generates a first mode signal corresponding to sunny weather or a second mode signal corresponding to rainy, snowy, or foggy weather, and uploads the generated weather type signal to the data acquisition module and the decision planning module.
[0012] Preferably, when the data acquisition module receives the first mode signal, the data acquisition module prioritizes acquiring the vehicle's own operating data and the roadside edge device unit data; when the data acquisition module receives the second mode signal, the data acquisition module prioritizes acquiring the historical data information of the preceding and following vehicles uploaded by the roadside edge device unit data, the vehicle's own operating data, the current data information of the preceding and following vehicles received via broadcast, and map information.
[0013] Preferably, the vehicle control module is used to receive and execute control commands formulated by the decision planning module to control the autonomous driving of the intelligent connected vehicle.
[0014] Preferably, the decision planning module has a verification function. When the vehicle imitates the trajectory of the preceding vehicle, if the change in the relative distance between the two vehicles exceeds a set threshold within a preset verification time, the trajectory imitation is determined to be unsuccessful. The decision planning module then regenerates control commands based on the target safe distance and the target speed.
[0015] Preferably, the environmental perception module uses sensor confidence level as the basis for weather judgment. When the sensor confidence level is lower than a set threshold, the environmental perception module determines that the weather type is severe weather such as rain, snow, or fog, and the environmental perception module generates a second mode signal.
[0016] Preferably, the decision planning module further includes a fault-safe strategy. When both the data acquisition module and the decision planning module are receiving signals uploaded by the environmental perception module, the decision planning module executes the fault-safe strategy.
[0017] Preferably, the decision planning module determines that the speed of the vehicle in front is lower than the vehicle's expected speed within a preset time period. If the road conditions permit, it determines whether the vehicle has an opportunity to overtake. When the vehicle has an opportunity to overtake, the decision planning module formulates a new driving route and driving speed, and generates an overtaking instruction.
[0018] The present invention has the following beneficial effects: 1. The system dynamically switches data source priorities based on weather type. In clear weather, it prioritizes the use of low-latency data from edge units to achieve high-precision determination of distances between vehicles and safe zones. In severe weather conditions such as rain, snow, and fog, it predicts the positions of vehicles on the road and prioritizes the integration of vehicle status information to compensate for the attenuation of its own sensors, thereby improving the robustness and reliability of the system in extreme environments.
[0019] 2. By imitating the trajectory of the same vehicle model and verifying it on-board, the reliability of following other vehicles in high-speed driving scenarios is improved, and accidents caused by differences in vehicle models are effectively reduced. When the following vehicle is overtaking, it can actively calculate and formulate a cooperative avoidance path based on the current situation, reduce the collision risk caused by the following vehicle overtaking, and improve the safety of intelligent driving. Attached Figure Description
[0020] Figure 1 A framework diagram of an intelligent connected vehicle autonomous driving detection and control system provided by the present invention; Figure 2 This is a working diagram of an intelligent connected vehicle autonomous driving detection and control system proposed in this invention; Figure 3 This is a diagram illustrating the working principle of the decision-making and planning module. Detailed Implementation
[0021] The technical solution of the present invention will now be clearly and completely described in conjunction with preferred embodiments. Obviously, the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.
[0022] like Figures 1 to 3 An intelligent connected vehicle autonomous driving detection and control system adopts a modular architecture, including an environmental perception module, a data acquisition module, a decision planning module, a vehicle control module, and a communication module. The environmental perception module interacts with the data acquisition module and the decision planning module; the data acquisition module interacts with the decision planning module; the decision planning module interacts with the vehicle control module; and the various modules interact with each other through the communication module. The communication module also enables data interaction between the vehicle and edge units, as well as between the vehicle and vehicles in front and behind. Furthermore, the vehicle interacts with vehicles in front and behind via broadcast. The vehicle receives basic safety information from vehicles in front and behind via broadcast, including vehicle dimensions, dynamic status (such as speed, direction of travel, and steering status), and vehicle type.
[0023] The environmental perception module acquires and determines current weather information, generating either a first-mode signal or a second-mode signal based on this information. Specifically, the environmental perception module distinguishes between clear weather and rain / snow / fog weather, generating a first-mode signal corresponding to clear weather or a second-mode signal corresponding to rain / snow / fog weather. This generated weather type signal is then uploaded to the data acquisition module and the decision-making and planning module. The environmental perception module uses sensor confidence level as the basis for weather judgment. When the sensor confidence level is below a set threshold, the environmental perception module determines the weather type as severe rain / snow / fog and generates a second-mode signal; conversely, when the confidence level is above a threshold, the environmental perception module determines the weather type as clear weather and generates a first-mode signal. This application utilizes the environmental perception module to provide a basis for subsequent decision-making.
[0024] The data acquisition module collects data from vehicles ahead and behind, the vehicle's own operational data, roadside edge device unit data, and map information. It prioritizes different data types based on weather conditions and collects and uploads data accordingly. When the data acquisition module receives a first-mode signal, it prioritizes collecting the vehicle's own operational data and roadside edge device unit data. When it receives a second-mode signal, it prioritizes collecting historical data from vehicles ahead and behind uploaded by roadside edge device units, the vehicle's own operational data, current data from vehicles ahead and behind received via broadcast, and map information. Furthermore, the data acquisition module uses existing technology to perform timestamp correction and predictive compensation on the collected data, ensuring that data from different sources are time-aligned, allowing current data to reflect future conditions.
[0025] The decision-making and planning module can automatically switch control strategies based on weather type. It has a first control strategy corresponding to clear weather and a second control strategy corresponding to rain, snow, and fog. When the module receives a first-mode signal, it activates the first control strategy; when it receives a second-mode signal, it activates the second control strategy. This application dynamically adjusts the control strategy according to weather type, effectively solving the problems of perception failure and decision misjudgment in severe weather in existing technologies, and significantly improving the system's robustness and survivability in extreme environments.
[0026] The first control strategy is as follows: the decision planning module prioritizes receiving data transmitted from the edge unit, combines it with the information of the vehicles in front and behind collected by the data acquisition module, determines the distance between the vehicle and the vehicles in front and behind, adjusts the vehicle's speed, and determines the safe driving area.
[0027] The system determines the vehicle model compared to the preceding vehicle. When both vehicles are the same model, the decision-making and planning module controls the vehicle to mimic the preceding vehicle's trajectory while ensuring safe driving. It also has a verification function: based on feedback data, within a preset verification time, it assesses the change in the relative distance between the two vehicles while mimicking the preceding vehicle's trajectory. If the change in relative distance exceeds a set threshold, the trajectory mimicry is deemed a failure, and the decision-making and planning module regenerates control commands based on the target safe distance and target speed. When the preceding vehicle's model differs from the current vehicle's, the decision-making and planning module abandons the side beam mimicking the preceding vehicle's trajectory, dynamically adjusts the current vehicle's safe distance based on the model difference, and makes independent decisions based on the vehicle's sensors. This improves following stability in high-speed driving scenarios and reduces the risk of scrapes and rear-end collisions.
[0028] The second control strategy is as follows: The decision planning module prioritizes receiving historical data information of the front and rear vehicles uploaded by the roadside edge equipment unit, map information, information of the front and rear vehicles received via broadcast, and the vehicle's own operating data. Combining the map information and the information uploaded by the front and rear vehicles to the roadside edge equipment unit, it predicts the positions of the front and rear vehicles and their distances from the vehicle, determines the safe driving area, and adjusts the vehicle speed based on the information of the front and rear vehicles received via broadcast, generating corresponding driving control commands.
[0029] Furthermore, when a vehicle behind attempts to overtake, the decision-making and planning module comprehensively identifies and analyzes the safe driving area, formulates a new driving path within the safe driving area, and generates corresponding control commands. The decision-making and planning module determines that the speed of the vehicle ahead is lower than the vehicle's desired speed within a preset time period. If road conditions permit, it assesses whether the vehicle has an overtaking opportunity. If the vehicle has an overtaking opportunity, the decision-making and planning module formulates a new driving path and speed, and generates an overtaking command.
[0030] The vehicle control module receives and executes control commands formulated by the decision planning module to control the autonomous driving of the intelligent connected vehicle. The data acquisition module collects driving data generated by the vehicle based on the control commands and uploads this driving data as feedback data to the decision planning module. The decision planning module adjusts the vehicle's driving state and optimizes driving commands based on the feedback information.
[0031] As described above, this application dynamically switches the data source priority according to weather type. In clear weather, it prioritizes the use of low-latency data from edge units to achieve high-precision determination of the distance between vehicles and the front and rear vehicles and the safe zone. In severe weather conditions with low visibility, such as rain, snow, and fog, it predicts the position of vehicles on the road and prioritizes the integration of vehicle status information to compensate for the attenuation of its own sensors, thereby improving the robustness and reliability of the system in extreme environments.
[0032] This application improves the reliability of following other vehicles in high-speed driving scenarios by imitating the trajectory of the same vehicle model and verifying it on-board, effectively reducing accidents caused by differences in vehicle models; when the following vehicle is overtaking, it can actively calculate and formulate a cooperative avoidance path according to the current situation, reducing the collision risk caused by the following vehicle overtaking and improving the safety of intelligent driving.
[0033] The system also includes an alarm module and a human-machine interface module. When neither the data acquisition module nor the decision-making and planning module receives a signal from the environmental perception module, the system determines that the environmental perception module has malfunctioned. The decision-making and planning module then executes a fail-safe strategy, controlling the alarm module to issue an alarm, prompting the driver to take over steering and braking. Simultaneously, the system gradually reduces the vehicle speed to allow the driver time to take over. If the driver does not intervene, the system will stop the car in a safe location. The system can continue to be used when the driver manually selects the control strategy mode of the decision-making and planning module through the human-machine interface module.
[0034] In summary, the workflow of this intelligent connected vehicle autonomous driving detection and control system is as follows: 1. Based on the current weather conditions, generate a weather type signal corresponding to the weather type, and upload the generated signal to the data acquisition module and the control decision module; 2. The data acquisition module determines the priority of data acquisition based on weather type, and performs data acquisition according to the data acquisition priority, and uploads the data to the control decision module; 3. The control decision module determines the control strategy to be executed based on the weather type, generates control commands based on the control strategy, and can adjust the driving status according to feedback data. 4. The vehicle control module receives and executes the control commands formulated by the decision planning module to control the autonomous driving of the intelligent connected vehicle.
[0035] All standard parts used in this invention can be purchased from the market, and irregularly shaped parts can be customized according to the description and drawings. The specific connection methods of each structure adopt conventional techniques such as bolt connections that are mature in the prior art. The machinery, parts and equipment adopt conventional models in the prior art. The materials and specifications of each component can be selected according to requirements and are not limited here. The contents not described in detail in this specification belong to the prior art known to those skilled in the art. Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The present invention will not be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. An intelligent networked vehicle automatic driving detection control system, comprising an environment perception module, a data acquisition module, a decision planning module, a vehicle control module and a communication module, characterized in that: The environmental perception module is used to acquire and determine the current weather information and generate a first mode signal or a second mode signal. The data acquisition module is used to collect data from vehicles in front and behind, vehicle operation data, roadside edge device unit data, and map information. It also determines the priority of different types of data collection based on weather type, and collects and uploads data according to the priority. When the decision planning module receives the first mode signal, it prioritizes receiving data transmitted by the edge unit, combines it with the information of the front and rear vehicles collected by the data acquisition module, determines the distance between the vehicle and the vehicles in front and behind, adjusts the vehicle's driving speed, and determines a safe driving area. When the preceding vehicle is of the same model as this vehicle, the decision-making and planning module controls this vehicle to mimic the trajectory of the preceding vehicle while ensuring safe driving. When the preceding vehicle is of a different model than this vehicle, the decision-making and planning module dynamically adjusts the safe distance of this vehicle according to the model difference and makes independent decisions based on the vehicle's sensors. When the decision-making and planning module receives the second mode signal, it prioritizes receiving historical data information of preceding and following vehicles uploaded by the roadside edge device unit, map information, information of preceding and following vehicles received via broadcast, and the vehicle's own driving data. Combining the map information and the information uploaded by preceding and following vehicles to the roadside edge device unit, it predicts the positions of preceding and following vehicles and their distances from this vehicle, determines the safe driving area, and adjusts the vehicle speed based on the information of preceding and following vehicles received via broadcast, generating corresponding driving control commands. Furthermore, when a following vehicle overtakes, the decision-making and planning module comprehensively identifies and analyzes the safe driving area, formulates a new driving path within the safe driving area, and generates corresponding control commands. 2.The intelligent networked vehicle automatic driving detection control system according to claim 1, characterized in that: The communication module is used to realize data interaction between the vehicle and the edge unit, and data interaction between the vehicle and the vehicles in front and behind, and the vehicle and the vehicles in front and behind realize data interaction through broadcast. 3.The intelligent networked vehicle automatic driving detection control system of claim 2, wherein: The vehicle receives basic safety information from the vehicles in front and behind via broadcast, including vehicle size, dynamic status, and vehicle type.
4. The intelligent connected vehicle autonomous driving detection and control system according to claim 1, characterized in that: The environmental perception module is used to distinguish between sunny weather and rainy, snowy, or foggy weather, and generates a first mode signal corresponding to sunny weather or a second mode signal corresponding to rainy, snowy, or foggy weather. The generated weather type signal is then uploaded to the data acquisition module and the decision planning module.
5. The intelligent connected vehicle autonomous driving detection and control system according to claim 1, characterized in that: When the data acquisition module receives the first mode signal, it prioritizes collecting the vehicle's own operating data and the roadside edge device unit data. When the data acquisition module receives the second mode signal, it prioritizes collecting the historical data information of the preceding and following vehicles uploaded by the roadside edge device unit, the vehicle's own operating data, the current data information of the preceding and following vehicles received via broadcast, and map information.
6. The intelligent connected vehicle autonomous driving detection and control system according to claim 1, characterized in that: The vehicle control module is used to receive and execute control commands formulated by the decision planning module to control the autonomous driving of intelligent connected vehicles.
7. The intelligent connected vehicle autonomous driving detection and control system according to claim 1, characterized in that: The decision planning module has a verification function. When the vehicle imitates the trajectory of the preceding vehicle, if the change in the relative distance between the two vehicles exceeds a set threshold within a preset verification time, the trajectory imitation is determined to be unsuccessful. The decision planning module then regenerates control commands based on the target safe distance and the target speed.
8. The intelligent connected vehicle autonomous driving detection and control system according to claim 4, characterized in that: The environmental perception module uses sensor confidence level as the basis for weather judgment. When the sensor confidence level is lower than a set threshold, the environmental perception module determines that the weather type is severe weather such as rain, snow, or fog, and generates a second mode signal.
9. The intelligent connected vehicle autonomous driving detection and control system according to claim 1, characterized in that: The decision planning module also includes a fault-safe strategy. When both the data acquisition module and the decision planning module are receiving signals uploaded by the environmental perception module, the decision planning module executes the fault-safe strategy.
10. The intelligent connected vehicle autonomous driving detection and control system according to claim 1, characterized in that: The decision planning module determines that the speed of the vehicle in front is lower than the vehicle's expected speed within a preset time period. If the road conditions permit, it determines whether the vehicle has an opportunity to overtake. When the vehicle has an opportunity to overtake, the decision planning module formulates a new driving route and speed, and generates an overtaking instruction.