Abstract: A method for vehicle place recognition, in which LiDAR data of a vehicle is processed to obtain a point cloud result of a vehicle LiDAR. An environmental descriptor is constructed based on the point cloud result. Based on a type of a LiDAR sensor of the LiDAR data and a measurement environment, a dynamic filtering strategy threshold is calculated. Compared the environmental descriptor and the dynamic filtering strategy threshold with point cloud data in a historical database, a vehicle position information is obtained.

Abstract: A control method of mixed traffic flow on freeway ramp based on controllable connected and autonomous vehicles (CAVs) is provided. A ramp is divided into a normal driving section, a vehicle platoon formation section and an accelerating and merging section. A vehicle platoon is formed by a leading CAV and human-driven vehicles (HDVs). Time interval [tmin, tmax] for the vehicle platoon to completely reach the merging point S is calculated. CAVs on the main lane and ramp are cooperatively controlled, and a merging gap is reserved for the ramp vehicle platoon. The vehicle platoon is allowed to accelerate and merge into the main lane. By means of the Internet-of-Vehicle (IoV) technology, the traffic situation on the main lane and downstream merging zone can be obtained in advance, and speeds of the CAVs are cooperatively controlled to lead the ramp vehicles to safely merge into the main lane.

Abstract: A fault-tolerant intelligent connected vehicle collaborative positioning method and system are provided, which relates to the field of intelligent connected vehicle positioning technologies. An iterative fusion of Gaussian belief propagation is performed on pre-processed information of longitude, latitude and altitude of a current position of a vehicle, data of angular velocity, traveling velocity, acceleration, wheel speed, steering angle and mileage of the vehicle and calibrated estimated value of the relative distance between the vehicle and a cooperative vehicle, to obtain a resultant position of the vehicle. By fusing measurement values of various sensors including the information of longitude, latitude and odometer of the vehicle, and vehicle-to-vehicle distance, integrity of collaborative positioning data is ensured. A fault detection and elimination method is performed in a graph model, which ensures fault-tolerance of the collaborative positioning system.

Abstract: The present disclosure provides a vehicle positioning method and system in a weak GNSS environment. The method includes: preprocessing system operation data information of an autonomous vehicle, where the system operation data information includes latitude and longitude data acquired from the GNSS system and three-axis acceleration, three-axis angular velocity and heading information acquired from an INS system; training a position prediction network with the preprocessed system operation data information acquired from the INS system to finally output supervision information of the prediction network; training a position correction network according to the supervision information in step S2 and the preprocessed system operation data information and the supervision information of the position correction network; and finally outputting a predicted value for correction.

Abstract: A hybrid traffic flow testing method and system based on digital twin and virtual-physical integration includes steps as follows. Data of a realistic testing site is collected, and a virtual scene is created based on the digital twin, interactions between a realistic environment and a virtual environment are set up to achieve a system configuration of a system based on the virtual-physical integration. Virtual human-driven vehicles are generated based on a data set of the system configuration, and realistic human-driven vehicles are generated based on data collected by driving simulators operated by realistic human drivers. Human-driven vehicles are formed by combining the virtual and realistic human-driven vehicles, and status and location information of the human-driven vehicles are acquired. A set of sensing, positioning, planning, control, and V2X communication methods are set up based on the status and the location information for CAVs, thereby achieving autonomous driving.

Abstract: A control method of mixed traffic flow on freeway ramp based on controllable connected and autonomous vehicles (CAVs) is provided. A ramp is divided into a normal driving section, a vehicle platoon formation section and an accelerating and merging section. A vehicle platoon is formed by a leading CAV and human-driven vehicles (HDVs). Time interval [tmin, tmax] for the vehicle platoon to completely reach the merging point S is calculated. CAVs on the main lane and ramp are cooperatively controlled, and a merging gap is reserved for the ramp vehicle platoon. The vehicle platoon is allowed to accelerate and merge into the main lane. By means of the Internet-of-Vehicle (IoV) technology, the traffic situation on the main lane and downstream merging zone can be obtained in advance, and speeds of the CAVs are cooperatively controlled to lead the ramp vehicles to safely merge into the main lane.

Abstract: A model of a system of an intelligent vehicle is trained and optimized using system operation data of the intelligent vehicle in a normal running state. The system operation data of the intelligent vehicle in a running state is collected in real time. Sensor data of the system operation data is de-noised, and feature extraction and screening are performed for a fatal sensor fault to reconstruct the system operation data. The reconstructed system operation data is inputted into the trained model to output system state data of the intelligent vehicle in the running state. The system state data is compared with a set threshold. If the system state data exceeds the set threshold, an actuator corresponding to the system state data is determined to have a fault. In addition, a system for a fault diagnosis of the intelligent vehicle is further provided.

Abstract: A model of a system of an intelligent vehicle is trained and optimized using system operation data of the intelligent vehicle in a normal running state. The system operation data of the intelligent vehicle in a running state is collected in real time. Sensor data of the system operation data is de-noised, and feature extraction and screening are performed for a fatal sensor fault to reconstruct the system operation data. The reconstructed system operation data is inputted into the trained model to output system state data of the intelligent vehicle in the running state. The system state data is compared with a set threshold. If the system state data exceeds the set threshold, an actuator corresponding to the system state data is determined to have a fault. In addition, a system for a fault diagnosis of the intelligent vehicle is further provided.