Datasets › PIE

PIE (Pedestrian Intention Estimation)

1 Oct 2019 archive 2025-07-28

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. Rich 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). There are over 300K labeled video frames with 1842 pedestrian samples making this the largest publicly available dataset for studying pedestrian behavior in traffic.

Benchmarks archive 2025-07-28

All 1 leaderboard whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

First row (archive order)PaperCode
Trajectory Prediction PIE SGNet MSE(0.5) 34 Stepwise Goal-Driven Networks for Trajectory Prediction ChuhuaW/SGNet.pytorch 5 Compare

Papers archive 2025-07-28

5 shown of 5 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 6. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

Dataset loaders archive 2025-07-28

No loader listed in the archive.

Tasks archive 2025-07-28

License archive 2025-07-28

MIT

Modalities archive 2025-07-28

No modality tagged.

Languages archive 2025-07-28

No language tagged.

Variants archive 2025-07-28

  • PIE

1 variant name, as the archive lists them.

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