In order to locate the position of intelligent connected vehicles (ICVs) robustly in urban intersection scenarios, a novel compound positioning method based on the Dempster-Shafer evidence theory and the improved multi-layer unscented Kalman filter (DS-UKF) is proposed in this paper. The Euclidean distance between the heterogeneous data is used to optimize the Dempster-Shafer (D-S) evidence theory, so that the position of ICVs and the distance between ICVs and roadside positioning platform with higher confidence can be obtained. Meanwhile, considering the high complication of urban intersections, the unscented Kalman filter (UKF) algorithm is improved based on the covariance of error sequences so that the influences caused by observation noise anomalies can be reduced. To further improve the accuracy of positional prediction, the position of roadside positioning platform is extended dimensionally into the state vector of ICVs to observe the distance between ICVs and roadside positioning platform. Then the particle filter (PF) theory is introduced to integrate the upper-layer with the lower-layer UKF to reduce the positioning errors brought by signal interferences. Besides, an adaptive factor is adopted to connect D-S theory with the improved multi-layer UKF (IUKF) to dynamically update the weights of output values from sensors. Furthermore, a communication mechanism between the ICVs platform and the roadside positioning platform is established in this project with considering the reliability and low latency of vehicle to everything (V2X) communications. Finally, the feasibility and superiority of the proposed method is validated by experiments with three ICVs at an urban intersection. The experimental results have shown that the proposed method can locate the position of ICVs rapidly and stably in urban intersections and has better positioning performance compared to the current state-of-the-art algorithms.

At present, the COVID-19 pandemic still presents with outbreaks occasionally, and pedestrians in public areas are at risk of being infected by the viruses. In order to reduce the risk of cross-infection, an advanced pedestrian state sensing method for automated patrol vehicles based on multi-sensor fusion is proposed to sense pedestrian state. Firstly, the pedestrian data output by the Euclidean clustering algorithm and the YOLO V4 network are obtained, and a decision-level fusion method is adopted to improve the accuracy of pedestrian detection. Then, combined with the pedestrian detection results, we calculate the crowd density distribution based on multi-layer fusion and estimate the crowd density in the scenario according to the density distribution. In addition, once the crowd aggregates, the body temperature of the aggregated crowd is detected by a thermal infrared camera. Finally, based on the proposed method, an experiment with an automated patrol vehicle is designed to verify the accuracy and feasibility. The experimental results have shown that the mean accuracy of pedestrian detection is increased by 17.1% compared with using a single sensor. The area of crowd aggregation is divided, and the mean error of the crowd density estimation is 3.74%. The maximum error between the body temperature detection results and thermometer measurement results is less than 0.8°, and the abnormal temperature targets can be determined in the scenario, which can provide an efficient advanced pedestrian state sensing technique for the prevention and control area of an epidemic.