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.
|Number of pages
|IEEE Transactions on Intelligent Transportation Systems
|Early online date
|19 Dec 2023
|E-pub ahead of print - 19 Dec 2023