Deep Reinforcement Learning-Based Vehicle Driving Strategy to Reduce Crash Risks in Traffic Oscillations
Deep Reinforcement Learning-Based Vehicle Driving Strategy to Reduce Crash Risks in Traffic Oscillations
复制标题
基于深度强化学习的车辆驾驶策略可降低交通振荡中的碰撞风险
DOI：
10.1177/0361198120937976
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发表时间：
2020-08
期刊：
影响因子：
--
通讯作者：
Tong Liu
中科院分区：
文献类型：
--
作者：
Meng Li;Zhibin Li;Chengcheng Xu;Tong Liu
本研究的主要目标是为个体车辆提出一种基于深度强化学习的驾驶策略，以减轻振荡并优化走走停停波中的交通安全。提出了一种基于深度确定性策略梯度（DDPG）的驾驶策略，该策略需要通过车载传感器直接获得的信息，用于系统性能优化。基于仿真软件SUMO，对两种典型情况进行了仿真：（1）前车根据真实的轨迹数据减速，产生一次振荡;（2）前车在不同扰动程度下进行多次突然减速，产生多次振荡。DDPG代理与SUMO平台进行交互，以确定车辆的最佳加速度，从而降低各种走走停停波中的碰撞风险。结果表明，基于DDPG的驾驶策略成功地降低了68.9%-78.4%的碰撞风险。比较了DDPG药剂渗透率不同和流量不同的情况，以测试所提出策略的效果。基于DDPG的驾驶策略随着车辆渗透率的增加，降低碰撞风险的效果更好，并且在高交通流率的场景中表现更好。将该策略与自适应巡航控制和阻塞吸收驾驶策略进行了比较。结果表明，所提出的策略优于其他振荡缓解策略，在降低碰撞风险。
The primary objective of this study is to propose a deep reinforcement learning-based driving strategy for individual vehicles to mitigate oscillations and optimize traffic safety in stop-and-go waves. A deep deterministic policy gradient (DDPG)-based driving strategy, which requires information that is directly obtained by in-vehicle sensors, is proposed for system performance optimization. Two typical scenarios were simulated based on simulation software (SUMO): (i) the leading vehicle slowed down according to real trajectory data to produce one oscillation; (ii) the leading vehicle conducted several abrupt decelerations with various degrees of disturbance to produce multiple oscillations. The DDPG agents interacted with the SUMO platform to determine the optimal acceleration of vehicles that can reduce crash risks in various stop-and-go waves. The results showed that the proposed DDPG-based driving strategy successfully reduced the crash risk by 68.9%–78.4%. Scenarios with different penetration rates of DDPG agents and in various flow rates were compared to test the effect of the proposed strategy. The DDPG-based driving strategy reduced crash risk more with the increase of penetration rate and this strategy performed better when applied in the scenario with a high traffic flow rate. The proposed strategy is compared with the adaptive cruise control and jam-absorbing driving strategies. Results showed the proposed strategy outperformed other oscillation mitigating strategies in reducing crash risks.
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DOI：
10.1609/aimag.v21i1.1501
发表时间：
2000-03
期刊：
AI Mag.
影响因子：
--
作者：
通讯作者：
--
DOI：
10.1109/tnn.1998.712192
发表时间：
1998
期刊：
IEEE Trans. Neural Networks
影响因子：
--
作者：
R. S. Sutton;A. Barto
通讯作者：
R. S. Sutton;A. Barto
影响因子：
5.9
作者：
Cunto, Flavio;Saccomanno, Frank F.
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Saccomanno, Frank F.
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--
作者：
J. Ploeg;A. Serrarens;G. Heijenk
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J. Ploeg;A. Serrarens;G. Heijenk
DOI：
--
发表时间：
2012-03
期刊：
--
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--
作者：
V. Astarita;G. Guido;A. Vitale;Vincenzo Pasquale Giofrè
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V. Astarita;G. Guido;A. Vitale;Vincenzo Pasquale Giofrè