An Integrated GNSS/UWB/DR/VMM Positioning Strategy for Intelligent Vehicles
Bing Zhu, Xiaowen Tao, Jian Zhao, Min Ke, Huaji Wang, Weiwen Deng
Abstract
Intelligent vehicle positioning is an enabling technology for decision-making, trajectory planning, and motion control, and is a key technology in intelligent transportation systems (ITSs). In order to obtain a low-cost and high-precision positioning strategy, an integrated positioning strategy for intelligent vehicles based on the global navigation satellite system (GNSS), dead reckoning (DR), ultra-wide band (UWB), and visual map matching (VMM) was proposed in this paper. First, the error sources of the three independent positioning methods of GNSS, DR, and UWB were analyzed, and independent positioning algorithms with improved accuracy were proposed. Then, the fusion positioning algorithm of GNSS/DR/UWB with an adaptive information distribution coefficient was established using a federated Kalman filter to improve the positioning accuracy and continuity. Afterward, to avoid reliance on the previous moment, the positioning result was further corrected by VMM. Finally, offline simulation and real vehicle tests were conducted under typical working conditions with the impact of real-world noise and the real-motion states of vehicles. The results showed that the GNSS/UWB/DR/VMM positioning algorithm could effectively improve the positioning accuracy and reliability of intelligent vehicles at a low cost.