  Inspired by human visual attention, we propose a novel inverse reinforcement
learning formulation using Maximum Entropy Deep Inverse Reinforcement Learning
(MEDIRL) for predicting the visual attention of drivers in accident-prone
situations. MEDIRL predicts fixation locations that lead to maximal rewards by
learning a task-sensitive reward function from eye fixation patterns recorded
from attentive drivers. Additionally, we introduce EyeCar, a new driver
attention dataset in accident-prone situations. We conduct comprehensive
experiments to evaluate our proposed model on three common benchmarks:
(DR(eye)VE, BDD-A, DADA-2000), and our EyeCar dataset. Results indicate that
MEDIRL outperforms existing models for predicting attention and achieves
state-of-the-art performance. We present extensive ablation studies to provide
more insights into different features of our proposed model.

Computer Vision and Pattern Recognition;Human-Computer Interaction;Machine Learning;Robotics