基于分布式深度强化学习的二次碰撞预防组合可变限速和变道引导

IF 2.4 3区 工程技术 Q3 TRANSPORTATION Journal of Transportation Safety & Security Pub Date : 2021-12-10 DOI:10.1080/19439962.2021.2011810
Chang Peng, Chengcheng Xu
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引用次数: 10

摘要

摘要本文的主要目标是开发一种可变限速(VSL)和变道引导(LCG)相结合的控制器,以防止高速公路上的二次碰撞(SCs),提高交通效率。VSL控制器提供速度限制指令和LCG控制器提供变道指令。提出了一种基于分布式深度强化学习的组合控制器。从安全性和效率两方面对组合控制器的性能进行了评价。仿真实验表明,由于VSL和LCG的互补,所开发的组合控制器总体上比任何单个子控制器都具有更高的性能。组合控制器中的VSL控制对SC预防和效率提高有先验效应,而LCG控制通过减少艰难变道次数和避免在相对不拥挤的条件下速度限制带来的额外SC风险,改善了VSL的缺点。此外,注意区域调查和灵敏度分析结果表明,所开发的控制器能够准确捕获先前碰撞造成的时空影响区域,并主动对交通流进行适当的干预。
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Combined variable speed limit and lane change guidance for secondary crash prevention using distributed deep reinforcement learning
Abstract The primary objective of this paper is to develop a combined variable speed limit (VSL) and lane change guidance (LCG) controller to prevent secondary crashes (SCs) and improve traffic efficiency on freeways. VSL controllers deliver speed limit instructions and LCG controllers deliver lane-changing instructions. A distributed deep reinforcement learning (RL)–based combined controller was proposed. The performance of the combined controller was evaluated in terms of safety and efficiency. Simulation experiments indicated that due to the complementation of VSL and LCG, the developed combined controller achieved higher performance in general than any single subcontroller. VSL control in a combined controller contributed prior effects on SC prevention and efficiency improvement, while LCG control improved the drawback of VSL by reducing the number of tough lane changes and avoiding extra SC risks caused by speed limit in relatively uncongested conditions. Moreover, the results of attention area investigation and sensitivity analysis revealed that the developed controller was able to accurately capture the spatial and temporal impact areas caused by prior crashes and generate proper interventions of traffic flow proactively.
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来源期刊
CiteScore
6.00
自引率
15.40%
发文量
38
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