{"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/optimal-alarms-for-vehicular-collision","title":"Optimal Alarms for Vehicular Collision Detection","arxiv_id":"1708.04922","date":"2017-08-16","proceeding":null,"authors":["Michael Motro","Joydeep Ghosh","Chandra Bhat"],"abstract":"An important application of intelligent vehicles is advance detection of\ndangerous events such as collisions. This problem is framed as a problem of\noptimal alarm choice given predictive models for vehicle location and motion.\nTechniques for real-time collision detection are surveyed and grouped into\nthree classes: random Monte Carlo sampling, faster deterministic\napproximations, and machine learning models trained by simulation. Theoretical\nguarantees on the performance of these collision detection techniques are\nprovided where possible, and empirical analysis is provided for two example\nscenarios. Results validate Monte Carlo sampling as a robust solution despite\nits simplicity.","url_abs":"http://arxiv.org/abs/1708.04922v1","url_pdf":"http://arxiv.org/pdf/1708.04922v1.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":[{"paper_slug":"optimal-alarms-for-vehicular-collision","repo_url":"https://github.com/utexas-ghosh-group/carstop","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}