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Decision-making in driver-automation shared control: A review and perspectives
IEEE/CAA Journal of Automatica Sinica ( IF 15.3 ) Pub Date : 2020-07-24 , DOI: 10.1109/jas.2020.1003294
Wenshuo Wang 1 , Xiaoxiang Na 2 , Dongpu Cao 3 , Jianwei Gong 4 , Junqiang Xi 4 , Yang Xing 5 , Fei-Yue Wang 6
Affiliation  

Shared control schemes allow a human driver to work with an automated driving agent in driver-vehicle systems while retaining the driverʼ s abilities to control. The human driver, as an essential agent in the driver-vehicle shared control systems, should be precisely modeled regarding their cognitive processes, control strategies, and decision-making processes. The interactive strategy design between drivers and automated driving agents brings an excellent challenge for human-centric driver assistance systems due to the inherent characteristics of humans. Many open-ended questions arise, such as what proper role of human drivers should act in a shared control scheme? How to make an intelligent decision capable of balancing the benefits of agents in shared control systems? Due to the advent of these attentions and questions, it is desirable to present a survey on the decision making between human drivers and highly automated vehicles, to understand their architectures, human driver modeling, and interaction strategies under the driver-vehicle shared schemes. Finally, we give a further discussion on the key future challenges and opportunities. They are likely to shape new potential research directions.

中文翻译:

驾驶员自动化共享控制中的决策:回顾与展望

共享的控制方案允许驾驶员在驾驶员-车辆系统中与自动驾驶代理一起工作,同时保留驾驶员的控制能力。作为驾驶员与车辆共享控制系统中的基本要素的人类驾驶员,应根据其认知过程,控制策略和决策过程进行精确建模。驾驶员与自动驾驶代理之间的交互策略设计由于以人为本的特性给以人为中心的驾驶员辅助系统带来了极大的挑战。出现了许多开放性问题,例如人类驾驶员在共享控制方案中应扮演什么适当角色?如何做出能够平衡共享控制系统中代理利益的智能决策?由于这些关注和问题的出现,理想的是对人类驾驶员和高度自动化的车辆之间的决策进行调查,以了解其结构,人类驾驶员模型以及驾驶员与车辆共享方案下的交互策略。最后,我们对未来关键的挑战和机遇进行了进一步的讨论。他们可能会塑造新的潜在研究方向。
更新日期:2020-08-04
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