Stop Line Aided Cooperative Positioning of Connected Vehicles
This work addresses positioning challenges for connected vehicles in intersection scenarios, offering an incremental improvement by integrating stop line data into cooperative navigation.
The paper tackles the problem of improving positioning accuracy for connected vehicles at intersections by using stop line information as a benchmark to correct GNSS/INS results, with experiments in Beijing showing enhanced performance for the vehicular network.
This paper develops a stop line aided cooperative positioning framework for connected vehicles, which creatively utilizes the location of the stop-line to achieve the positioning enhancement for a vehicular ad-hoc network (VANET) in intersection scenarios via Vehicle-to-Vehicle (V2V) communication. Firstly, a self-positioning correction scheme for the first stopped vehicle is presented, which applied the stop line information as benchmarks to correct the GNSS/INS positioning results. Then, the local observations of each vehicle are fused with the position estimates of other vehicles and the inter-vehicle distance measurements by using an extended Kalman filter (EKF). In this way, the benefits of the first stopped vehicle are extended to the whole VANET. Such a cooperative inertial navigation (CIN) framework can greatly improve the positioning performance of the VANET. Finally, experiments in Beijing show the effectiveness of the proposed stop line aided cooperative positioning framework.