State estimation of connected vehicles using a nonlinear ensemble filter

Liu Jiang(刘江)^{1, 2}, Chen Hua-zhan(陈华展)¹, Cai Bai-gen(蔡伯根)^{1, 2}, Wang Jian(王剑)^{1, 3}

(1. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China;
2. Beijing Engineering Research Center of EMC and GNSS Technology for Rail Transportation, Beijing 100044, China;
3. State Key Laboratory of Rail Traffic Control and Safety (Beijing Jiaotong University), Beijing 100044, China)

Abstract:The concept of connected vehicles is with great potentials for enhancing the road transportation systems in the future. To support the functions and applications under the connected vehicles frame, the estimation of dynamic states of the vehicles under the cooperative environments is a fundamental issue. By integrating multiple sensors, localization modules in OBUs (on-board units) require effective estimation solutions to cope with various operation conditions. Based on the filtering estimation framework for sensor fusion, an ensemble Kalman filter (EnKF) is introduced to estimate the vehicle’s state with observations from navigation satellites and neighborhood vehicles, and the original EnKF solution is improved by using the cubature transformation to fulfill the requirements of the nonlinearity approximation capability, where the conventional ensemble analysis operation in EnKF is modified to enhance the estimation performance without increasing the computational burden significantly. Simulation results from a nonlinear case and the cooperative vehicle localization scenario illustrate the capability of the proposed filter, which is crucial to realize the active safety of connected vehicles in future intelligent transportation.

Key words:connected vehicles; state estimation; cooperative positioning; nonlinear ensemble filter; global navigation satellite system (GNSS); dedicated short range communication (DSRC)