{"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/long-term-on-board-prediction-of-people-in","title":"Long-Term On-Board Prediction of People in Traffic Scenes under Uncertainty","arxiv_id":"1711.09026","date":"2017-11-24","proceeding":"CVPR 2018 6","authors":["Apratim Bhattacharyya","Mario Fritz","Bernt Schiele"],"abstract":"Progress towards advanced systems for assisted and autonomous driving is\nleveraging recent advances in recognition and segmentation methods. Yet, we are\nstill facing challenges in bringing reliable driving to inner cities, as those\nare composed of highly dynamic scenes observed from a moving platform at\nconsiderable speeds. Anticipation becomes a key element in order to react\ntimely and prevent accidents. In this paper we argue that it is necessary to\npredict at least 1 second and we thus propose a new model that jointly predicts\nego motion and people trajectories over such large time horizons. We pay\nparticular attention to modeling the uncertainty of our estimates arising from\nthe non-deterministic nature of natural traffic scenes. Our experimental\nresults show that it is indeed possible to predict people trajectories at the\ndesired time horizons and that our uncertainty estimates are informative of the\nprediction error. We also show that both sequence modeling of trajectories as\nwell as our novel method of long term odometry prediction are essential for\nbest performance.","url_abs":"http://arxiv.org/abs/1711.09026v2","url_pdf":"http://arxiv.org/pdf/1711.09026v2.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":"abstracts"},"code_links":[],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"trajectory-prediction","task_name":"Trajectory Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/trajectory-prediction-on-jaad","task":"Trajectory Prediction","dataset":"JAAD","model":"Bayesian-LSTM","rank_in_archive_order":5,"of":5,"metrics":{"CF_MSE(1.5)":"5615","C_MSE(1.5)":"1447","MSE(0.5)":"159","MSE(1.0)":"539","MSE(1.5)":"1535"},"uses_additional_data":false},{"leaderboard":"/sota/trajectory-prediction-on-pie","task":"Trajectory Prediction","dataset":"PIE","model":"Bayesian-LSTM","rank_in_archive_order":5,"of":5,"metrics":{"CF_MSE(1.5)":"5615","C_MSE(1.5)":"1447","MSE(0.5)":"159","MSE(1.0)":"539","MSE(1.5)":"1535"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1711.09026","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}