When vehicles are in driving environments such as urban roads and tunnels, the global navigation satellite system (GNSS) signal can fail to achieve positioning due to occlusion. Cooperative positioning can effectively improve the accuracy and coverage of vehicle positioning. In this paper, we address the vehicle localization issue in GNSS-Loss driving environments and with random packet loss due to wireless communications. We propose an improved cooperative localization algorithm based on belief propagation, which first linearizes the measurement model of distance by statistical linear regression method, then performs belief propagation based on the linearized model, and solves the packet loss problem by expanding the dimension of the vehicle state. From the simulation results, the algorithm can effectively reduce the impact of packet loss and improve the vehicle localization accuracy.
In this paper we describe a hardware-in-the-loop platform (HIL) for evaluating the performance of On-Board-Unit (OBU) in C-V2X scenario. We use CARLA as the simulation environment to obtain high fidelity scenario data. In this platform we design the basic evaluation scenarios based on current standards and then design random scenario parameters for obtaining a large number of test scenarios with different parameters to test the robustness of the algorithm. The key scenario parameters are stored while the scenario is running and are used to analyze and evaluate the performance of the algorithm at the end of the scenario and to produce an evaluation report, a use case is provided in the text to demonstrate the overall usefulness of the platform.
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