{"title":"基于多车道标记检测和水平保护水平的高完整性车道定位","authors":"Gabriel Frisch, Philippe Xu, E. Stawiarski","doi":"10.1109/ICARCV.2018.8581278","DOIUrl":null,"url":null,"abstract":"For autonomous driving, lane level accurate localization is a necessity for complex driving maneuvers. Classical GNSS based methods are usually not accurate enough to have an unambiguous lane level localization. Having camera measurements such as lane marking detections along with high definition maps can enhance localization performance. In this paper, we are interested in high integrity localization, meaning being robust with low risk level. We propose a novel geometrical approach using horizontal protection levels on localization to propagate uncertainties and use lane markings to have an unambiguous map-matching. We demonstrate on real data that the algorithm can cope with high levels of noise on both localization and detection.","PeriodicalId":395380,"journal":{"name":"2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"High Integrity Lane Level Localization Using Multiple Lane Markings Detection and Horizontal Protection Levels\",\"authors\":\"Gabriel Frisch, Philippe Xu, E. Stawiarski\",\"doi\":\"10.1109/ICARCV.2018.8581278\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For autonomous driving, lane level accurate localization is a necessity for complex driving maneuvers. Classical GNSS based methods are usually not accurate enough to have an unambiguous lane level localization. Having camera measurements such as lane marking detections along with high definition maps can enhance localization performance. In this paper, we are interested in high integrity localization, meaning being robust with low risk level. We propose a novel geometrical approach using horizontal protection levels on localization to propagate uncertainties and use lane markings to have an unambiguous map-matching. We demonstrate on real data that the algorithm can cope with high levels of noise on both localization and detection.\",\"PeriodicalId\":395380,\"journal\":{\"name\":\"2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICARCV.2018.8581278\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICARCV.2018.8581278","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
High Integrity Lane Level Localization Using Multiple Lane Markings Detection and Horizontal Protection Levels
For autonomous driving, lane level accurate localization is a necessity for complex driving maneuvers. Classical GNSS based methods are usually not accurate enough to have an unambiguous lane level localization. Having camera measurements such as lane marking detections along with high definition maps can enhance localization performance. In this paper, we are interested in high integrity localization, meaning being robust with low risk level. We propose a novel geometrical approach using horizontal protection levels on localization to propagate uncertainties and use lane markings to have an unambiguous map-matching. We demonstrate on real data that the algorithm can cope with high levels of noise on both localization and detection.