{"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/simulation-based-adversarial-test-generation","title":"Simulation-based Adversarial Test Generation for Autonomous Vehicles with Machine Learning Components","arxiv_id":"1804.06760","date":"2018-04-18","proceeding":null,"authors":["Cumhur Erkan Tuncali","Georgios Fainekos","Hisahiro Ito","James Kapinski"],"abstract":"Many organizations are developing autonomous driving systems, which are\nexpected to be deployed at a large scale in the near future. Despite this,\nthere is a lack of agreement on appropriate methods to test, debug, and certify\nthe performance of these systems. One of the main challenges is that many\nautonomous driving systems have machine learning components, such as deep\nneural networks, for which formal properties are difficult to characterize. We\npresent a testing framework that is compatible with test case generation and\nautomatic falsification methods, which are used to evaluate cyber-physical\nsystems. We demonstrate how the framework can be used to evaluate closed-loop\nproperties of an autonomous driving system model that includes the ML\ncomponents, all within a virtual environment. We demonstrate how to use test\ncase generation methods, such as covering arrays, as well as requirement\nfalsification methods to automatically identify problematic test scenarios. The\nresulting framework can be used to increase the reliability of autonomous\ndriving systems.","url_abs":"http://arxiv.org/abs/1804.06760v4","url_pdf":"http://arxiv.org/pdf/1804.06760v4.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":"simulation-based-adversarial-test-generation","repo_url":"https://github.com/saitejavn/lgsvl-scenarios","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"simulation-based-adversarial-test-generation","repo_url":"https://github.com/tuncalie/sim-atav","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"autonomous-vehicles","task_name":"Autonomous Vehicles"},{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.06760","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}