{"url":"/dataset/pie","name":"PIE","full_name":"Pedestrian Intention Estimation","description_markdown":"PIE is a new dataset for studying pedestrian behavior in traffic. PIE contains over 6 hours of footage recorded in typical traffic scenes with on-board camera. It also provides accurate vehicle information from OBD sensor (vehicle speed, heading direction and GPS coordinates) synchronized with video footage.\r\nRich spatial and behavioral annotations are available for pedestrians and vehicles that potentially interact with the ego-vehicle as well as for the relevant elements of infrastructure (traffic lights, signs and zebra crossings).\r\nThere are over 300K labeled video frames with 1842 pedestrian samples making this the largest publicly available dataset for studying pedestrian behavior in traffic.","description_withheld":null,"homepage":"https://data.nvision2.eecs.yorku.ca/PIE_dataset/","introduced_date":"2019-10-01","introduced_date_note":null,"introduced_by":null,"license":{"name":"MIT","url":"https://github.com/aras62/PIE/blob/master/LICENSE"},"modalities":[],"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":["PIE"],"data_loaders":[],"num_papers_in_archive":6,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/trajectory-prediction-on-pie","task":"Trajectory Prediction","dataset_variant":"PIE","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)":"1761","C_MSE(1.5)":"413","MSE(0.5)":"34","MSE(1.0)":"133","MSE(1.5)":"442"},"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."}