Abstract: The present application discloses a traffic light control method for an urban road network based on expected return estimation, which uses C-V2X wireless communication technology to obtain real-time information of all vehicles and traffic state in the road network from vehicle-mounted terminals, and adaptively and dynamically controls the phase transformation of the traffic light. According to the present application, the expected returns of keeping the current phase and executing phase switch are calculated by estimating the timely driving distance and the future driving distance of the passable vehicles in the next green light duration in combination with the proposed road priority traffic index. By comparing the expected returns of keeping the current phase or switching to other phases, the best phase is selected, so as to make as many passable vehicles travel farther as possible in the next green light duration. Therefore, the efficiency of traffic will be improved.

Abstract: The present application discloses a method and a device for estimating the position of a networked vehicle based on independent non-uniform increment sampling. By mapping a laser radar point cloud to a spatiotemporal aligned image, independent non-uniform increment sampling is carried out on the mapping points falling in an advanced semantic constraint region of the image according to a point density of the depth interval where the mapping points are located, and the virtual mapping points generated by sampling are reversely mapped to the original point cloud space and merged with the original point cloud, and the combined point cloud is used to estimate the position of the networked vehicle based on a deep learning method, so as to solve the inaccurate position estimation problem of sheltered or remote networked vehicles due to the sparseness or missing of its own point cloud clusters.

Abstract: The present application discloses a visual enhancement method and a system based on fusion of spatially aligned features of multiple networked vehicles. The method utilizes the visual features of networked vehicles themselves and their surroundings within a certain range, and performs feature fusion after spatial alignment to realize visual expansion and enhancement. After receiving the compressed intermediate feature map of the networked vehicles in a certain range around, the decompressed intermediate feature map is subjected to affine transformation, and the transformed aligned feature map is subjected to feature fusion based on a designed multi-feature self-learning network, so as to realize the complementation and enhancement among features while removing redundant features. The fused intermediate features are used to detect the target obstacles from the perspective of the networked vehicle itself and partially or completely blocked., thus improving the safety of driving connected vehicles.

Abstract: The present application discloses a traffic light control method for an urban road network based on expected return estimation, which uses C-V2X wireless communication technology to obtain real-time information of all vehicles and traffic state in the road network from vehicle-mounted terminals, and adaptively and dynamically controls the phase transformation of the traffic light. According to the present application, the expected returns of keeping the current phase and executing phase switch are calculated by estimating the timely driving distance and the future driving distance of the passable vehicles in the next green light duration in combination with the proposed road priority traffic index. By comparing the expected returns of keeping the current phase or switching to other phases, the best phase is selected, so as to make as many passable vehicles travel farther as possible in the next green light duration. Therefore, the efficiency of traffic will be improved.

Abstract: Disclosed is a two-phase access authentication method integrating spatial-temporal features in space-air-ground integrated networks. In the method, an access authentication is divided into two phases: a primary authentication phase and a continued authentication phase. In the primary authentication phase, a user equipment and a satellite are respectively initialized and registered through a ground network control center. In the authentication phase, a fast and secure access is achieved by using a user ID, facial features, and other authentication factors. In the continued authentication phase, data of a user flow and behavior features are acquired, and feature comparison is performed by using historical user data; and a security level and an authentication decision are output.