The autonomous driving has shown its enormous potential to become the new generation of transportation in the last decade. Based on the automated technology, vehicles can drive in a new form, vehicle platoon, which can significantly increase the efficiency of the road system and save road resources. The space-vehicle traffic state estimation model has shown its benefits in modeling autonomous vehicle platoon in nonpipeline corridors with on- and off-ramps in ideal observation environment. However, in the current initial stage of automated connected vehicles’ application, the observation environment is quite imperfect. Limited by financial and investment, traffic flow observation equipment is sparsely distributed on the road. How to adapt to the sparse observer layout is a critical issue in the current application of the space-time traffic state estimation, which is originally designed for the autonomous transportation. Therefore, this manuscript overviews the observation environment in practice and summarizes three key observation problems. This article designs 22 numerical experiments focusing on the three key issues and implements the space-time estimation model in different observation scenarios. Finally, the observation environment adaptability is analyzed in detail based on the experiment results. It is found that the accuracy of the estimation results can be improved with the highest efficiency under the premise of limited equipment input by reducing the observation interval to 1000 m and increasing the density of the observer to 1/km. For the road sections with relatively homogeneous traffic conditions, the layout of observation equipment can be relatively reduced to save the investment input. Also, the maintenance of observation equipment for the ramp with larger flow can be slowed down appropriately in limited equipment investment. This manuscript is of great practical significance to the popularization and application of connected automatic transportation.

中文翻译：

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稀疏分布观测环境中空间交通状态估计模型的适应性分析

在过去的十年中，自动驾驶技术已显示出其成为新一代交通运输工具的巨大潜力。基于自动化技术，车辆可以以一种新的形式（车辆排）行驶，这可以显着提高道路系统的效率并节省道路资源。航天器交通状态估计模型已显示出其在模拟理想观测环境中具有上下坡道的非管道走廊中的自动驾驶汽车排中建模的优势。但是，在目前自动联网汽车应用的初始阶段，观测环境还很不完善。受资金和投资的限制，交通流量观察设备很少分布在道路上。在时空交通状态估计的当前应用中，如何适应稀疏的观察者布局是一个关键问题，而空时交通状态估计最初是为自动交通设计的。因此，该手稿概述了实践中的观测环境，并总结了三个关键的观测问题。本文针对这三个关键问题设计了22个数值实验，并在不同的观察场景中实现了时空估计模型。最后，根据实验结果对观测环境的适应性进行了详细分析。发现在有限的设备输入的前提下，通过将观察间隔减小到1000 m，并将观察者的密度增加到1 / km，可以以最高的效率提高估计结果的准确性。对于交通条件相对均匀的路段，可以相对减少观测设备的布置，以节省投资投入。另外，在有限的设备投资中，可以适当地放慢对流量较大的坡道的观察设备的维护。该手稿对于互联自动运输的推广和应用具有重要的现实意义。