{"title":"前车状态预测","authors":"Rohit Pandita, D. Caveney","doi":"10.1109/IVS.2013.6629597","DOIUrl":null,"url":null,"abstract":"A model-based approach is presented for predicting future state (position and velocity) of the preceding vehicle in response to velocity disturbance from lead vehicle in a platoon. Online parameter estimation is used to adapt model parameters based on characteristics of individual drivers in the platoon. A car-following model is used to describe platoon longitudinal dynamics. Examples are presented using simulated as well as real-traffic data.","PeriodicalId":251198,"journal":{"name":"2013 IEEE Intelligent Vehicles Symposium (IV)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"Preceding vehicle state prediction\",\"authors\":\"Rohit Pandita, D. Caveney\",\"doi\":\"10.1109/IVS.2013.6629597\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A model-based approach is presented for predicting future state (position and velocity) of the preceding vehicle in response to velocity disturbance from lead vehicle in a platoon. Online parameter estimation is used to adapt model parameters based on characteristics of individual drivers in the platoon. A car-following model is used to describe platoon longitudinal dynamics. Examples are presented using simulated as well as real-traffic data.\",\"PeriodicalId\":251198,\"journal\":{\"name\":\"2013 IEEE Intelligent Vehicles Symposium (IV)\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-10-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE Intelligent Vehicles Symposium (IV)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IVS.2013.6629597\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE Intelligent Vehicles Symposium (IV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IVS.2013.6629597","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A model-based approach is presented for predicting future state (position and velocity) of the preceding vehicle in response to velocity disturbance from lead vehicle in a platoon. Online parameter estimation is used to adapt model parameters based on characteristics of individual drivers in the platoon. A car-following model is used to describe platoon longitudinal dynamics. Examples are presented using simulated as well as real-traffic data.