{"url":"/sota/trajectory-prediction-on-sdd","task":{"name":"Trajectory Prediction","url":"/task/trajectory-prediction","note":null},"dataset":{"name":"SDD","url":"/dataset/sdd"},"category":"Computer Vision","categories":["Computer Vision","Time Series"],"category_note":null,"description":"**Trajectory Prediction** is the problem of predicting the short-term (1-3 seconds) and long-term (3-5 seconds) spatial coordinates of various road-agents such as cars, buses, pedestrians, rickshaws, and animals, etc. These road-agents have different dynamic behaviors that may correspond to aggressive or conservative driving styles.\r\n\r\n\r\n<span class=\"description-source\">Source: [Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMs ](https://arxiv.org/abs/1912.01118)</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["mADEK @4.8s","mF DEK @4.8s"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"mADEK @4.8s":null,"mF DEK @4.8s":null}},"counts":{"rows":1,"rows_with_code":1,"rows_with_paper_page":1,"rows_dated":1,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"Multiclass-SGCN (ours)","metrics":{"mADEK @4.8s":"14.36","mF DEK @4.8s":"25.99"},"uses_additional_data":false,"paper_date":"2022-06-30","paper":"/paper/multiclass-sgcn-sparse-graph-based-trajectory","paper_url":"https://arxiv.org/abs/2206.15275v1","paper_title":"Multiclass-SGCN: Sparse Graph-based Trajectory Prediction with Agent Class Embedding","code":"https://github.com/carrotsniper/multiclass-sgcn","n_code_links":1,"syntology":null}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}