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

机译:通过无源特征的联合感测,增强了协作的车辆定位

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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,如全球定位系统(GPS)或伽利略,是车辆定位最常见和可接近的技术。然而,在密集的城市地区,即使与车载车载传感器结合使用,由于多径和信号堵塞,它们会导致大量的本地化误差。近年来,合作智能交通系统(C-ITS)由于允许车辆通过车辆到车辆(V2V)通信而允许车辆与邻居合作和广播有关的安全相关信息。在本文中,通过在不使用显式V2V测距的情况下开发V2V通信开发了一种新颖的合作定位方法。以分布式方式定位车辆,以分布式方式,一组共同感测的非协作特征(例如,人,交通灯),并使用它们作为常见的嘈杂参考点,以隐含地提高自己的位置准确性。分布式信念传播与基于共识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,尤其是在具有密集特征部署的情况下。

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