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首页> 外文期刊>IEEE Transactions on Intelligent Transportation Systems >Augmenting Vehicle Localization by Cooperative Sensing of the Driving Environment: Insight on Data Association in Urban Traffic Scenarios
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Augmenting Vehicle Localization by Cooperative Sensing of the Driving Environment: Insight on Data Association in Urban Traffic Scenarios

机译：通过协作传感来增强车辆本地化的驾驶环境：城市交通方案数据关联的洞察力

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摘要

Precise vehicle positioning is a key element for the development of Cooperative Intelligent Transport Systems (C-ITS). In this context, we present a distributed processing technique to augment the performance of conventional Global Navigation Satellite Systems (GNSS) exploiting Vehicle-to-anything (V2X) communication systems. We propose a method, referred to as Implicit Cooperative Positioning with Data Association (ICP-DA), where the connected vehicles detect a set of passive features in the driving environment, solve the association task by pairing them with on-board sensor measurements and cooperatively localize the features to enhance the GNSS accuracy. We adopt a belief propagation algorithm to distribute the processing over the network, and solve both the data association and localization problems locally at vehicles. Numerical results on realistic traffic networks show that the ICP-DA method is able to significantly outperform the conventional GNSS. In particular, the analysis on a real urban road infrastructure highlights the robustness of the proposed method in real-life cases where the interactions among vehicles evolve over space and time according to traffic regulation mechanisms. Performances are investigated both in conventional traffic-light regulated scenarios and self-regulated environments (as representative of future automated driving scenarios) where vehicles autonomously cross the intersections taking gap-availability decisions for avoiding collisions. The analysis shows how the mutual coordination in platoons of vehicles eases the cooperation process and increases the positioning performance.
机译：精确的车辆定位是合作智能传输系统（C-ITS）的开发的关键要素。在这种情况下，我们提出了一种分布式处理技术，以增加传统的全球导航卫星系统（GNSS）利用车辆到任何（V2X）通信系统的性能。我们提出了一种方法，称为具有数据关联（ICP-DA）的隐式协作定位的方法，其中连接的车辆检测到驱动环境中的一组被动功能，通过将它们与车载传感器测量配对并协同地解决关联任务本地化功能以增强GNSS精度。我们采用信仰传播算法来分发网络上的处理，并在车辆本地解决数据关联和本地化问题。现实交通网络上的数值结果表明，ICP-DA方法能够显着优于传统的GNSS。特别是，实际城市道路基础设施的分析突出了所提出的方法在现实生活中所提出的方法的鲁棒性，其中车辆之间的相互作用根据交通规程机制而在空间和时间上发展。在传统的交通灯调节场景和自我调节环境中研究了性能（作为未来自动化驾驶场景的代表），其中车辆自主地跨越用于避免冲突的差距可用性决策的交叉点。分析表明，车辆夹板中的相互协调如何缓解合作过程并提高定位性能。

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