A safe transition between autonomous and manual control requires sustained visual attention of the driver for the perception and assessment of hazards in dynamic driving environments. Thus, drivers must retain a certain level of situation awareness to safely takeover. Understanding the visual attention allocation of drivers can pave the way for inferring their dynamic state of situational awareness. We propose a reinforcement and inverse-reinforcement learning framework for modeling passive drivers' visual attention allocation in semi-autonomous vehicles. The proposed approach measures the eye-movement of passive drivers to evaluate their responses to real-world rear-end collisions. The results show substantial individual differences in the eye fixation patterns by driving experience, even among fully attentive drivers. Experienced drivers were more attentive to the situational dynamics and were able to identify potentially hazardous objects before any collisions occurred. These models of visual attention could potentially be integrated into autonomous systems to continuously monitor and guide effective intervention. Keywords: Visual attention allocation; Situation awareness; Eye movements; Eye fixation; Eye-Tracking; Reinforcement Learning; Inverse Reinforcement Learning

本文提出了一种基于最大熵深度逆强化学习（MEDIRL）的方法，用于预测事故易发情况下驾驶员的视觉注意力；介绍了EyeCar，一种新的记录事故情况下驾驶员的注意力数据集；通过在三个常见基准测试（DR(eye)VE，BDD-A，DADA-2000）和EyeCar数据集上的全面实验，结果表明MEDIRL在预测注意力方面优于现有模型，并获得了最先进的表现；提供了详细的消融实验，以更深入地了解我们提出的模型的不同特征。

MEDIRL：基于最大熵深度逆强化学习预测驾驶员的视觉注意力