{"title":"一种用于动力系统长视界速度预测的纵向驾驶员模型","authors":"F. Morlock, O. Sawodny","doi":"10.1109/ICCVE45908.2019.8965215","DOIUrl":null,"url":null,"abstract":"Two major emerging fields of research in automotive engineering are autonomous driving and electromobility. The predictive or intelligent longitudinal control within the former and consumption forecasts for the latter are dependent on lookahead data provided by cloud based services as real time road and traffic data. Furthermore, these applications can be improved by customization to the driver. This paper proposes a simple, yet accurate parametric model for longitudinal driving characteristics which is designed for use in powertrain applications that could be a predictive, intelligent cruise controller or a personalized consumption forecast. A methodology for offline identification of the model parameters is presented that can be easily transferred to online implementation. The model is validated against measurement data and meaningful metrics for assessing its performance are introduced. It is shown that predicted speed yields good resemblance to measurements.","PeriodicalId":384049,"journal":{"name":"2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A longitudinal driver model for long horizon speed prediction in powertrain applications\",\"authors\":\"F. Morlock, O. Sawodny\",\"doi\":\"10.1109/ICCVE45908.2019.8965215\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Two major emerging fields of research in automotive engineering are autonomous driving and electromobility. The predictive or intelligent longitudinal control within the former and consumption forecasts for the latter are dependent on lookahead data provided by cloud based services as real time road and traffic data. Furthermore, these applications can be improved by customization to the driver. This paper proposes a simple, yet accurate parametric model for longitudinal driving characteristics which is designed for use in powertrain applications that could be a predictive, intelligent cruise controller or a personalized consumption forecast. A methodology for offline identification of the model parameters is presented that can be easily transferred to online implementation. The model is validated against measurement data and meaningful metrics for assessing its performance are introduced. It is shown that predicted speed yields good resemblance to measurements.\",\"PeriodicalId\":384049,\"journal\":{\"name\":\"2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE)\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCVE45908.2019.8965215\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCVE45908.2019.8965215","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A longitudinal driver model for long horizon speed prediction in powertrain applications
Two major emerging fields of research in automotive engineering are autonomous driving and electromobility. The predictive or intelligent longitudinal control within the former and consumption forecasts for the latter are dependent on lookahead data provided by cloud based services as real time road and traffic data. Furthermore, these applications can be improved by customization to the driver. This paper proposes a simple, yet accurate parametric model for longitudinal driving characteristics which is designed for use in powertrain applications that could be a predictive, intelligent cruise controller or a personalized consumption forecast. A methodology for offline identification of the model parameters is presented that can be easily transferred to online implementation. The model is validated against measurement data and meaningful metrics for assessing its performance are introduced. It is shown that predicted speed yields good resemblance to measurements.