Personalized Adaptive Cruise Control and Impacts on Mixed Traffic
Personalized Adaptive Cruise Control and Impacts on Mixed Traffic
复制标题
个性化自适应巡航控制及其对混合交通的影响
DOI：
10.23919/acc50511.2021.9482812
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Yao Ma
中科院分区：
文献类型：
--
作者：
M. Ozkan;Yao Ma
提出了一种能够学习人类驾驶员行为并在跟车场景下自适应控制半自动驾驶汽车的个性化自适应巡航控制（PACC）设计，并研究了其对混合交通的影响。在SAV和人类驾驶车辆共享道路的混合交通中，SAV的驾驶员可以选择更适合驾驶员首选驾驶策略的PACC调谐。通过逆向强化学习（IRL）方法，通过从驾驶员演示的驾驶数据中恢复一个独特的成本函数来学习个体驾驶员的偏好，该成本函数最好地解释了观察到的驾驶风格。所提出的PACC设计计划的SAV的运动，通过最小化学习的唯一成本函数，考虑到前面的人类驾驶车辆的短预览信息。实验结果表明，在各种交通条件下，学习后的驾驶员模型能够准确、一致地识别和复制前车的个性化驾驶行为。此外，我们研究了PACC与不同的司机对混合交通的影响，考虑时间车头时距，间隙距离，和燃油经济性的评估。统计调查表明，PACC对混合交通的影响因驾驶员的内在驾驶偏好而异。
This paper presents a personalized adaptive cruise control (PACC) design that can learn human driver behavior and adaptively control the semi-autonomous vehicle (SAV) in the car-following scenario, and investigates its impacts on mixed traffic. In mixed traffic where the SAV and human-driven vehicles share the road, the SAV's driver can choose a PACC tuning that better fits the driver's preferred driving strategies. The individual driver's preferences are learned through the inverse reinforcement learning (IRL) approach by recovering a unique cost function from the driver's demonstrated driving data that best explains the observed driving style. The proposed PACC design plans the motion of the SAV by minimizing the learned unique cost function considering the short preview information of the preceding human-driven vehicle. The results reveal that the learned driver model can identify and replicate the personalized driving behaviors accurately and consistently when following the preceding vehicle in a variety of traffic conditions. Furthermore, we investigated the impacts of the PACC with different drivers on mixed traffic by considering time headway, gap distance, and fuel economy assessments. A statistical investigation shows that the impacts of the PACC on mixed traffic vary among tested drivers due to their intrinsic driving preferences.
影响因子：
8.2
作者：
Bolduc, Andrew Phillip;Guo, Longxiang;Jia, Yunyi
通讯作者：
Jia, Yunyi