{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:40:48Z","timestamp":1723016448133},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:p>This paper addresses the challenge of generating safety-critical scenarios with multiple adversarial vehicles for testing autonomous vehicles.\n\nSuch scenarios must be plausible and collision-avoidable while resulting in a collision with the vehicle-under-test.\n\nHowever, the tremendous number of scenarios and the low ratio of plausible scenarios makes previous methods squander primary resources on implausible scenarios, degenerating their efficiency.\n\nWe propose a two-stage framework called the ASP-based Avoidable Collision Scenario Testbench (A\u00b2CoST) to overcome this obstacle and improve efficiency.\n\nIn the former stage, we apply Answer Set Programming (ASP) for generating plausible logical scenarios.\n\nIn the latter stage, we use a search algorithm to refine logical scenarios into safety-critical concrete scenarios.\n\nWe also compute collision-free trajectories in these concrete scenarios while the vehicle-under-test fails to avoid the collision.\n\nWe empirically show the A\u00b2CoST significantly decreases the time consumption for simple scenarios while still effectively generating complex critical scenarios.\n\nThe comparison with real-world traffic data further demonstrates the value of A\u00b2CoST in generating plausible scenarios.\n\nThe source codes of our method and the baselines are opened at https:\/\/github.com\/Autonomous-Driving-Safety-Project\/AACoST.<\/jats:p>","DOI":"10.24963\/kr.2023\/67","type":"proceedings-article","created":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T22:27:47Z","timestamp":1690842467000},"page":"690-699","source":"Crossref","is-referenced-by-count":0,"title":["A\u00b2CoST: An ASP-based Avoidable Collision Scenario Testbench for Autonomous Vehicles"],"prefix":"10.24963","author":[{"given":"Ruolin","family":"Wang","sequence":"first","affiliation":[{"name":"University of Science and Technology of China"}]},{"given":"Yuejiao","family":"Xu","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China"}]},{"given":"Jie","family":"Peng","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China"}]},{"given":"Jianmin","family":"Ji","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China"}]}],"member":"10584","event":{"number":"20","sponsor":["Artificial Intelligence Journal","Principles of Knowledge Representation and Reasoning Inc.","Academic College of Tel-Aviv","European Association for Artificial Intelligence","National Science Foundation"],"acronym":"KR-2023","name":"20th International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}","start":{"date-parts":[[2023,9,2]]},"theme":"Artificial Intelligence","location":"Rhodes, Greece","end":{"date-parts":[[2023,9,8]]}},"container-title":["Proceedings of the Twentieth International Conference on Principles of Knowledge Representation and Reasoning"],"original-title":[],"deposited":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T22:29:03Z","timestamp":1690842543000},"score":1,"resource":{"primary":{"URL":"https:\/\/proceedings.kr.org\/2023\/67"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2023,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/kr.2023\/67","relation":{},"subject":[],"published":{"date-parts":[[2023,9]]}}}