{"title":"辅助驾驶变道意图预测:道路设计与评价","authors":"B. Morris, A. Doshi, M. Trivedi","doi":"10.1109/IVS.2011.5940538","DOIUrl":null,"url":null,"abstract":"Automobiles are quickly becoming more complex as new sensors and support systems are being added to improve safety and comfort. The next generation of intelligent driver assistance systems will need to utilize this wide array of sensors to fully understand the driving context and situation. Effective interaction requires these systems to examine the intentions, desires, and needs of the driver for preemptive actions which can help prepare for or avoid dangerous situations. This manuscript develops a real-time on-road prediction system able to detect a driver's intention to change lanes seconds before it occurs. In-depth analysis highlights the challenges when moving intent prediction from the laboratory to the road and provides detailed characterization of on-road performance.","PeriodicalId":117811,"journal":{"name":"2011 IEEE Intelligent Vehicles Symposium (IV)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"194","resultStr":"{\"title\":\"Lane change intent prediction for driver assistance: On-road design and evaluation\",\"authors\":\"B. Morris, A. Doshi, M. Trivedi\",\"doi\":\"10.1109/IVS.2011.5940538\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Automobiles are quickly becoming more complex as new sensors and support systems are being added to improve safety and comfort. The next generation of intelligent driver assistance systems will need to utilize this wide array of sensors to fully understand the driving context and situation. Effective interaction requires these systems to examine the intentions, desires, and needs of the driver for preemptive actions which can help prepare for or avoid dangerous situations. This manuscript develops a real-time on-road prediction system able to detect a driver's intention to change lanes seconds before it occurs. In-depth analysis highlights the challenges when moving intent prediction from the laboratory to the road and provides detailed characterization of on-road performance.\",\"PeriodicalId\":117811,\"journal\":{\"name\":\"2011 IEEE Intelligent Vehicles Symposium (IV)\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-06-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"194\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE Intelligent Vehicles Symposium (IV)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IVS.2011.5940538\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE Intelligent Vehicles Symposium (IV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IVS.2011.5940538","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Lane change intent prediction for driver assistance: On-road design and evaluation
Automobiles are quickly becoming more complex as new sensors and support systems are being added to improve safety and comfort. The next generation of intelligent driver assistance systems will need to utilize this wide array of sensors to fully understand the driving context and situation. Effective interaction requires these systems to examine the intentions, desires, and needs of the driver for preemptive actions which can help prepare for or avoid dangerous situations. This manuscript develops a real-time on-road prediction system able to detect a driver's intention to change lanes seconds before it occurs. In-depth analysis highlights the challenges when moving intent prediction from the laboratory to the road and provides detailed characterization of on-road performance.