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.

中文翻译：

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通过驾驶环境的协同感知增强车辆定位：洞察城市交通场景中的数据关联

精确的车辆定位是合作智能交通系统 (C-ITS) 发展的关键要素。在这种情况下，我们提出了一种分布式处理技术，以增强利用车辆对任何事物 (V2X) 通信系统的传统全球导航卫星系统 (GNSS) 的性能。我们提出了一种称为数据关联隐式协作定位 (ICP-DA) 的方法，其中连接的车辆检测驾驶环境中的一组被动特征，通过将它们与车载传感器测量值配对并协作来解决关联任务定位特征以提高 GNSS 精度。我们采用置信传播算法在网络上分配处理，并在车辆本地解决数据关联和定位问题。现实交通网络的数值结果表明，ICP-DA 方法能够显着优于传统的 GNSS。特别是，对真实城市道路基础设施的分析突出了所提出方法在现实生活中的鲁棒性，在现实生活中，车辆之间的相互作用根据交通监管机制在空间和时间上演进。在传统的交通灯管制场景和自我调节环境（作为未来自动驾驶场景的代表）中，车辆自动穿过交叉路口，采取间隙可用性决策以避免碰撞，对性能进行了研究。分析显示了车辆排的相互协调如何简化合作过程并提高定位性能。