{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-benchmark-for-unsupervised-anomaly","title":"A Benchmark for Unsupervised Anomaly Detection in Multi-Agent Trajectories","arxiv_id":"2209.01838","date":"2022-09-05","proceeding":null,"authors":["Julian Wiederer","Julian Schmidt","Ulrich Kressel","Klaus Dietmayer","Vasileios Belagiannis"],"abstract":"Human intuition allows to detect abnormal driving scenarios in situations they never experienced before. Like humans detect those abnormal situations and take countermeasures to prevent collisions, self-driving cars need anomaly detection mechanisms. However, the literature lacks a standard benchmark for the comparison of anomaly detection algorithms. We fill the gap and propose the R-U-MAAD benchmark for unsupervised anomaly detection in multi-agent trajectories. The goal is to learn a representation of the normal driving from the training sequences without labels, and afterwards detect anomalies. We use the Argoverse Motion Forecasting dataset for the training and propose a test dataset of 160 sequences with human-annotated anomalies in urban environments. To this end we combine a replay of real-world trajectories and scene-dependent abnormal driving in the simulation. In our experiments we compare 11 baselines including linear models, deep auto-encoders and one-class classification models using standard anomaly detection metrics. The deep reconstruction and end-to-end one-class methods show promising results. The benchmark and the baseline models will be publicly available.","url_abs":"https://arxiv.org/abs/2209.01838v1","url_pdf":"https://arxiv.org/pdf/2209.01838v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"a-benchmark-for-unsupervised-anomaly","repo_url":"https://github.com/againerju/r_u_maad","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}