{"title":"A proposal for Neuro-ITS over the connected vehicles network","authors":"M. Sasaki","doi":"10.1109/IVS.2015.7225653","DOIUrl":null,"url":null,"abstract":"On the basis of the recent technical trend of automated vehicles and connected vehicles, we have been proposing the new application concept Neuro-ITS which has three major technical features such as multi-viewpoint tracking, human appearance prediction, and collective intelligence. Especially by combining the collective intelligence with sensing control, we will drastically reduce the hectic tasks to collect and teach huge size of GT (ground truth) which has been serious bottleneck of conventional machine learning. Also it will greatly improve the performance of environment understanding beyond perception. In this article, we focus on the collective intelligence and investigate the technical realization regarding the evolutionary process of ET (estimated truth) towards GT.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE Intelligent Vehicles Symposium (IV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IVS.2015.7225653","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
On the basis of the recent technical trend of automated vehicles and connected vehicles, we have been proposing the new application concept Neuro-ITS which has three major technical features such as multi-viewpoint tracking, human appearance prediction, and collective intelligence. Especially by combining the collective intelligence with sensing control, we will drastically reduce the hectic tasks to collect and teach huge size of GT (ground truth) which has been serious bottleneck of conventional machine learning. Also it will greatly improve the performance of environment understanding beyond perception. In this article, we focus on the collective intelligence and investigate the technical realization regarding the evolutionary process of ET (estimated truth) towards GT.