Reducing driver's behavioural uncertainties using an interdisciplinary approach: Convergence of Quantified Self, Automated Vehicles, Internet Of Things and Artificial Intelligence.

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• 作者：A. Rakotonirainy^* ; ^{r.andry@qut.edu.au} ; O. Orfila^** ; ^{olivier.orfila@ifsttar.fr} ; D. Gruyer^** ; ^{dominique.gruyer@ifsttar.fr}
• 关键词：automated driving ; quantified self ; internet of things ; artificial intelligence
• 刊名：IFAC-PapersOnLine
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：49
• 期：32
• 页码：78-82
• 全文大小：491 K
• 卷排序：49

文摘

Growing research progress in Internet of Things (IoT), automated/connected cars, Artificial Intelligence and person's data acquisition (Quantified Self) will help to reduce behavioral uncertainties in transport and unequivocally influence future transport landscapes. This vision paper argues that by capitalizing advances in data collection and methodologies from emerging research disciplines, we could make the driver amenable to a knowable and monitorable entity, which will improve road safety. We present an interdisciplinary framework, inspired by the Safe system, to extract knowledge from the large amount of available data during driving. The limitation of our approach is discussed.