A Fusion Strategy for Vehicle Positioning at Intersections Utilizing UWB and Onboard Sensors.

For vehicle positioning applications in Intelligent Transportation Systems (ITS), lane-level or even more precise localization is desired in some typical urban scenarios. With the rapid development of wireless positioning technologies, ultrawide bandwidth (UWB) has stood out and become a prominent approach for high-precision positioning. However, in traffic scenarios, the UWB-based positioning method may deteriorate because of not-line-of-sight (NLOS) propagation, multipath effect and other external interference. To overcome these problems, in this paper, a fusion strategy utilizing UWB and onboard sensors is developed to achieve reliable and precise vehicle positioning. It is a two-step approach, which includes the preprocessing of UWB raw measurements and the global estimation of vehicle position. Firstly, an ARIMA-GARCH model to address the NLOS problem of UWB at vehicular traffic scenarios is developed, and then the NLOS of UWB can be detected and corrected efficiently. Further, an adaptive IMM algorithm is developed to realize global fusion. Compared with traditional IMM, the proposed AIMM is capable of adjusting the model probabilities to make them better matching for current driving conditions, then positioning accuracy can be improved. Finally, the method is validated through experiments. Field test results verify the effectiveness and feasibility of the proposed strategy.

Tightly-Coupled Vehicle Positioning Method at Intersections Aided by UWB.

Reliable and precise vehicle positioning is essential for most intelligent transportation applications as well as autonomous driving. Due to satellite signal blocking, it can be challenging to achieve continuous lane-level positioning in GPS-denied environments such as urban canyons and crossroads. In this paper, a positioning strategy utilizing ultra-wide band (UWB) and low-cost onboard sensors is proposed, aimed at tracking vehicles in typical urban scenarios (such as intersections). UWB tech offers the potential of achieving high ranging accuracy through its ability to resolve multipath and penetrate obstacles. However, not line of sight (NLOS) propagation still has a high occurrence in intricate urban intersections and may significantly deteriorate positioning accuracy. Hence, we present an autoregressive integrated moving average (ARIMA) model to first address the NLOS problem. Then, we propose a tightly-coupled multi sensor fusion algorithm, in which the fuzzy calibration logic (FCL) is designed and introduced to adaptively adjust the dependence on each received UWB measurement to effectively mitigate NLOS and multipath interferences. At last, the proposed strategy is evaluated through experiments. Ground test results validate that this low-cost approach has the potential to achieve accurate, reliable and continuous localization, regardless of the GPS working statue.