{"url":"/dataset/jaad","name":"JAAD","full_name":"Joint Attention in Autonomous Driving","description_markdown":"JAAD is a dataset for studying joint attention in the context of autonomous driving. The focus is on pedestrian and driver behaviors at the point of crossing and factors that influence them. To this end, JAAD dataset provides a richly annotated collection of 346 short video clips (5-10 sec long) extracted from over 240 hours of driving footage. These videos filmed in several locations in North America and Eastern Europe represent scenes typical for everyday urban driving in various weather conditions.\r\n\r\nBounding boxes with occlusion tags are provided for all pedestrians making this dataset suitable for pedestrian detection.\r\n\r\nBehavior annotations specify behaviors for pedestrians that interact with or require attention of the driver. For each video there are several tags (weather, locations, etc.) and timestamped behavior labels from a fixed list (e.g. stopped, walking, looking, etc.). In addition, a list of demographic attributes is provided for each pedestrian (e.g. age, gender, direction of motion, etc.) as well as a list of visible traffic scene elements (e.g. stop sign, traffic signal, etc.) for each frame.\r\n\r\nPaper: [Are They Going to Cross? A Benchmark Dataset and Baseline for Pedestrian Crosswalk Behavior](https://doi.org/10.1109/ICCVW.2017.33)\r\n\r\nSource: [JAAD](http://data.nvision2.eecs.yorku.ca/JAAD_dataset/)\r\n\r\nImage Source: [Are They Going to Cross? A Benchmark Dataset and Baseline for Pedestrian Crosswalk Behavior](https://doi.org/10.1109/ICCVW.2017.33)","description_withheld":null,"homepage":"http://data.nvision2.eecs.yorku.ca/JAAD_dataset/","introduced_date":"2017-02-12","introduced_date_note":null,"introduced_by":{"paper":null,"title":"Agreeing to Cross: How Drivers and Pedestrians Communicate","first_author":null,"url":null},"license":{"name":"MIT License","url":"https://github.com/ykotseruba/JAAD/blob/JAAD_2.0/LICENSE"},"modalities":[{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Trajectory Prediction","url":"/task/trajectory-prediction","datasets_with_task":"/datasets/task/trajectory-prediction"},{"name":"Multi-future Trajectory Prediction","url":"/task/multi-future-trajectory-prediction","datasets_with_task":"/datasets/task/multi-future-trajectory-prediction"},{"name":"Trajectory Forecasting","url":"/task/trajectory-forecasting","datasets_with_task":"/datasets/task/trajectory-forecasting"},{"name":"Pedestrian Trajectory Prediction","url":"/task/pedestrian-trajectory-prediction","datasets_with_task":"/datasets/task/pedestrian-trajectory-prediction"}],"languages":[],"variants":["JAAD"],"data_loaders":[],"num_papers_in_archive":21,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/trajectory-prediction-on-jaad","task":"Trajectory Prediction","dataset_variant":"JAAD","rows":5,"metrics":["MSE(0.5)","MSE(1.0)","MSE(1.5)","C_MSE(1.5)","CF_MSE(1.5)"],"first_row_in_archive_order":{"model":"SGNet","paper":"/paper/stepwise-goal-driven-networks-for-trajectory","metrics":{"CF_MSE(1.5)":"4076","C_MSE(1.5)":"996","MSE(0.5)":"82","MSE(1.0)":"328","MSE(1.5)":"1049"},"code_links":[{"title":"ChuhuaW/SGNet.pytorch","url":"https://github.com/ChuhuaW/SGNet.pytorch"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/stepwise-goal-driven-networks-for-trajectory","title":"Stepwise Goal-Driven Networks for Trajectory Prediction","date":"2021-03-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/bitrap-bi-directional-pedestrian-trajectory","title":"BiTraP: Bi-directional Pedestrian Trajectory Prediction with Multi-modal Goal Estimation","date":"2020-07-29","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/pie-a-large-scale-dataset-and-models-for","title":"PIE: A Large-Scale Dataset and Models for Pedestrian Intention Estimation and Trajectory Prediction","date":"2019-10-01","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/unsupervised-traffic-accident-detection-in","title":"Unsupervised Traffic Accident Detection in First-Person Videos","date":"2019-03-02","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":0,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/long-term-on-board-prediction-of-people-in","title":"Long-Term On-Board Prediction of People in Traffic Scenes under Uncertainty","date":"2017-11-24","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":5,"samples_ran":0,"samples_unverified":5,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":1,"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."}