A Markov Decision Process framework to incorporate network-level data in motion planning for connected and automated vehicles
A Markov Decision Process framework to incorporate network-level data in motion planning for connected and automated vehicles
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DOI：
10.1016/j.trc.2021.103550
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发表时间：
2022-03
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影响因子：
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通讯作者：
Xiangguo Liu;Neda Masoud;Qi Zhu;Anahita Khojandi
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文献类型：
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作者：
Xiangguo Liu;Neda Masoud;Qi Zhu;Anahita Khojandi
自动化和连通性有望提高运输系统的安全性和燃油效率。虽然协调巡航控制等联网车辆技术可以通过整合车辆视线之外的信息来改善车辆运动规划，但其好处受到当前仅利用本地信息的短视规划策略的限制。在本文中，我们提出了一个框架，设计车辆轨迹耦合局部最优的运动规划与马尔可夫决策过程（MDP）模型，可以捕获网络级的信息。我们提出的框架可以保证安全，同时最大限度地减少旅行的广义成本，其中包括其燃料和时间成本。为了展示在设计车辆轨迹时结合网络级数据的好处，我们在三个实验环境中进行了全面的模拟研究，即环形轨道，带有上下坡道的高速公路和小型城市网络。仿真结果表明，在不同的交通状态下，在所有的实验设置下，可以获得统计上显着的效率为主题的车辆及其周围的车辆。本文作为概念验证，展示了如何利用连接性和自主性将网络级信息纳入运动规划。
Autonomy and connectivity are expected to enhance safety and improve fuel efficiency in transportation systems. While connected vehicle-enabled technologies, such as coordinated cruise control, can improve vehicle motion planning by incorporating information beyond the line of sight of vehicles, their benefits are limited by the current short-sighted planning strategies that only utilize local information. In this paper, we propose a framework that devises vehicle trajectories by coupling a locally-optimal motion planner with a Markov decision process (MDP) model that can capture network-level information. Our proposed framework can guarantee safety while minimizing a trip’s generalized cost, which comprises of its fuel and time costs. To showcase the benefits of incorporating network-level data when devising vehicle trajectories, we conduct a comprehensive simulation study in three experimental settings, namely a circular track, a highway with on- and off-ramps, and a small urban network. The simulation results indicate that statistically significant efficiency can be obtained for the subject vehicle and its surrounding vehicles in different traffic states under all experimental settings. This paper serves as a proof-of-concept to showcase how connectivity and autonomy can be leveraged to incorporate network-level information into motion planning.