E²T-CVL: An Efficient and Error-Tolerant Approach for Collaborative Vehicle Localization

The proliferation of vehicle-to-vehicle (V2V) communication techniques has resulted in collaborative vehicle localization (CVL) approaches that localize a target vehicle by leveraging the state information of nearby vehicles. However, CVL approaches typically require a large search space to locate the real position of a target vehicle and assume small measurement errors of nearby vehicle information, which limit the efficiency and robustness of the existing methods. In this article, we propose an efficient and error-tolerant CVL approach (referred to as E2T-CVL) that increases localization efficiency and accuracy even in the case of large measurement errors of nearby vehicle information. Unlike the existing CVL approaches, our approach prunes the search space for the position of the target vehicle through a pruning-based strategy that considers the relative positions of nearby vehicles. To determine the position of the target vehicle from the search space, we propose a displacement-based selection method to reduce the influence of the measurement errors of nearby vehicle information. The localization accuracy and efficiency of the proposed approach are then evaluated using simulated global positioning system trajectories in a large road network in New York City. The experimental results show that the proposed approach achieves higher localization efficiency and greater accuracy even with large measurement errors compared to state-of-the-art CVL approaches.