{"url":"/dataset/eyecar","name":"EyeCar","full_name":null,"description_markdown":"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.\r\n\r\nSource: [MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning](https://arxiv.org/abs/1912.07773)","description_withheld":null,"homepage":"https://github.com/soniabaee/MEDIRL-EyeCar","introduced_date":"2019-12-17","introduced_date_note":null,"introduced_by":{"paper":"/paper/eyecar-modeling-the-visual-attention","title":"MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning","first_author":"Sonia Baee","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Autonomous Vehicles","url":"/task/autonomous-vehicles","datasets_with_task":"/datasets/task/autonomous-vehicles"}],"languages":[],"variants":["EyeCar"],"data_loaders":[{"repo":"https://github.com/soniabaee/MEDIRL-EyeCar","url":"https://github.com/soniabaee/MEDIRL-EyeCar","frameworks":[]}],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}