EyeCar
Introduced by Baee et al. in MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning
EyeCar is a dataset of driving videos of vehicles involved in rear-end collisions paired with eye fixation data captured from human subjects. It contains 21 front-view videos that were captured in various traffic, weather, and day light conditions. Each video is 30sec in length and contains typical driving tasks (e.g., lanekeeping, merging-in, and braking) ending to rear-end collisions.
Source: MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement LearningPapers
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