{"title":"意图感知运动规划与道路规则","authors":"J. Karlsson, Jana Tumova","doi":"10.1109/CASE48305.2020.9217037","DOIUrl":null,"url":null,"abstract":"We present an approach for intention-aware motion planning in an autonomous driving scenario, where a vehicle aims to traverse a road segment as quickly as possible, while constrained by road rules encoded in syntactically co-safe linear temporal logic. We show that by combining the RRTx algorithm with trajectory prediction using Mixed Observable Markov Decision Processes (MOMDP), we can achieve least-violating behavior wrt. mission completion time and the road rules, while ensuring that the likelihood of collisions remains below a user specified threshold. We illustrate the validity of our approach using simulations of a variety of traffic scenarios.","PeriodicalId":212181,"journal":{"name":"2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Intention-aware motion planning with road rules\",\"authors\":\"J. Karlsson, Jana Tumova\",\"doi\":\"10.1109/CASE48305.2020.9217037\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present an approach for intention-aware motion planning in an autonomous driving scenario, where a vehicle aims to traverse a road segment as quickly as possible, while constrained by road rules encoded in syntactically co-safe linear temporal logic. We show that by combining the RRTx algorithm with trajectory prediction using Mixed Observable Markov Decision Processes (MOMDP), we can achieve least-violating behavior wrt. mission completion time and the road rules, while ensuring that the likelihood of collisions remains below a user specified threshold. We illustrate the validity of our approach using simulations of a variety of traffic scenarios.\",\"PeriodicalId\":212181,\"journal\":{\"name\":\"2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CASE48305.2020.9217037\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CASE48305.2020.9217037","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We present an approach for intention-aware motion planning in an autonomous driving scenario, where a vehicle aims to traverse a road segment as quickly as possible, while constrained by road rules encoded in syntactically co-safe linear temporal logic. We show that by combining the RRTx algorithm with trajectory prediction using Mixed Observable Markov Decision Processes (MOMDP), we can achieve least-violating behavior wrt. mission completion time and the road rules, while ensuring that the likelihood of collisions remains below a user specified threshold. We illustrate the validity of our approach using simulations of a variety of traffic scenarios.