{"url":"/dataset/kitti-trajectory-prediction","name":"KITTI-trajectory-prediction","full_name":null,"description_markdown":"KITTI is a well established dataset in the computer vision community. It has often been used for trajectory prediction despite not having a well defined split, generating non comparable baselines in different works. This dataset aims at bridging this gap and proposes a well defined split of the KITTI data.\r\nSamples are collected as 6 seconds chunks (2seconds for past and 4 for future) in a sliding window fashion from all trajectories in the dataset, including the egovehicle. There are a total of 8613 top-view trajectories for training and 2907 for testing.\r\nSince top-view maps are not provided by KITTI, semantic labels of static categories obtained with DeepLab-v3+ from all frames are projected in a common top-view map using the Velodyne 3D point cloud and IMU. The resulting maps have a spatial resolution of 0.5 meters and are provided along with the trajectories.","description_withheld":null,"homepage":"https://github.com/Marchetz/KITTI-trajectory-prediction","introduced_date":"2020-06-05","introduced_date_note":null,"introduced_by":{"paper":"/paper/mantra-memory-augmented-networks-for-multiple-1","title":"MANTRA: Memory Augmented Networks for Multiple Trajectory Prediction","first_author":"Francesco Marchetti","url":null},"license":null,"modalities":[],"tasks":[{"name":"Multi Future Trajectory Prediction","url":"/task/multi-future-trajectory-prediction-1","datasets_with_task":"/datasets/task/multi-future-trajectory-prediction-1"}],"languages":[],"variants":["KITTI-trajectory-prediction"],"data_loaders":[{"repo":"https://github.com/Marchetz/KITTI-trajectory-prediction","url":"https://github.com/Marchetz/KITTI-trajectory-prediction","frameworks":["pytorch"]}],"num_papers_in_archive":3,"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":"2026-09-24T18:15:14+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."}