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Enhanced vehicle positioning in cooperative ITS by joint sensing of passive features

机译：通过联合感知被动特征来增强协作ITS中的车辆定位

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摘要

Satellite-based navigation systems, such as Global Positioning System (GPS) or Galileo, are the most common and accessible techniques for vehicle positioning. However, in dense urban areas, even if combined with vehicle on-board sensors, they lead to large localization errors due to multipath and signal blockage. In recent years, Cooperative Intelligent Transportation Systems (C-ITSs) have gained increasing attention as they allow vehicles to cooperate and broadcast safety-related information to the neighbors through Vehicle-to-Vehicle (V2V) communications. In this paper, a novel cooperative positioning method is developed by exploiting V2V communications without using explicit V2V ranging. Vehicles localize, in a distributed way, a set of jointly sensed non-cooperative features (e.g., people, traffic lights) and use them as common noisy reference points to implicitly enhance their own position accuracy. Distributed belief propagation is combined with consensus-based estimation of the features' positions to enable cooperative localization of vehicles. Numerical results show that the proposed method is able to significantly enhance the GPS-based vehicle location accuracy, especially in scenarios with dense feature deployments.
机译：基于卫星的导航系统，例如全球定位系统（GPS）或伽利略，是最常见且可访问的车辆定位技术。但是，在人口稠密的城市地区，即使与车辆车载传感器结合使用，由于多径和信号阻塞，它们也会导致较大的定位误差。近年来，协作智能交通系统（C-ITS）受到越来越多的关注，因为它们允许车辆进行协作，并通过车对车（V2V）通信向邻居广播安全相关的信息。在本文中，通过利用V2V通信而不使用显式的V2V测距，开发了一种新颖的协作定位方法。车辆以分布式方式定位一组共同感应的非合作特征（例如人，交通信号灯），并将它们用作常见的嘈杂参考点，以隐式提高其自身的位置精度。分布式信念传播与特征位置的基于共识的估计相结合，以实现车辆的协同定位。数值结果表明，该方法能够显着提高基于GPS的车辆定位精度，特别是在具有密集特征部署的场景中。

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