Traffic Flow Stabilization Strategy for Mitigating Automated and Human Driven Vehicles Interactions

B. Park, Seongah Hong
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Abstract

One of key challenges in the area of automated vehicles or self-driving cars is how to ensure smooth interactions between the automated vehicles and human driven vehicles. This is because it would be inevitable to have both automated vehicles and human driven vehicles until market penetration of automated vehicle reaches 100 percent. Our paper proposed traffic flow stabilization strategy based on optimal control theory and evaluated its performance using a microscopic traffic simulation tool under varying automated vehicle market penetrations. The simulation results indicated that the proposed approach effectively improves traffic flow stability when compared to the base case under adaptive cruise control algorithm.
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缓解自动驾驶和人驾驶车辆相互作用的交通流稳定策略
自动驾驶汽车或自动驾驶汽车领域的关键挑战之一是如何确保自动驾驶车辆与人类驾驶车辆之间的顺畅交互。这是因为,在自动驾驶汽车的市场渗透率达到100%之前,自动驾驶汽车和人工驾驶汽车都是不可避免的。本文提出了基于最优控制理论的交通流稳定策略,并利用微观交通仿真工具对其在不同自动驾驶汽车市场渗透率下的性能进行了评价。仿真结果表明,与自适应巡航控制算法相比,该方法有效地提高了交通流的稳定性。
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