Continual driver behaviour learning for connected vehicles and intelligent transportation systems: Framework, survey and challenges  

作　　者：Zirui Li Cheng Gong Yunlong Lin Guopeng Li Xinwei Wang Chao Lu Miao Wang Shanzhi Chen Jianwei Gong 

机构地区：[1]Chair of Traffic Process Automation,“Friedrich List”Faculty of Transport and Traffic Sciences,TU Dresden,01069 Dresden,Germany [2]School of Mechanical Engineering,Beijing Institute of Technology,100081 Beijing,China [3]Transport and Planning,Civil Engineering and Geosciences,Delft University of Technology,2628 CD Delft [4]School of Engineering and Materials Science,Queen Mary University of London,E14NS London,UK [5]Baidu Inc,Beijing 100085,China [6]State Key Laboratory of Wireless Mobile Communications,China Information and Communication Technology Group Co.,Ltd.(CICT),China

出　　处：《Green Energy and Intelligent Transportation》2023年第4期69-80,共12页新能源与智能载运（英文）

基　　金：Supported by the National Key Research and Development Program of China(No.2022ZD0115503).

摘　　要：Modelling,predicting and analysing driver behaviours are essential to advanced driver assistance systems(ADAS)and the comprehensive understanding of complex driving scenarios.Recently,with the development of deep learning(DL),numerous driver behaviour learning(DBL)methods have been proposed and applied in connected vehicles(CV)and intelligent transportation systems(ITS).This study provides a review of DBL,which mainly focuses on typical applications in CV and ITS.First,a comprehensive review of the state-of-the-art DBL is presented.Next,Given the constantly changing nature of real driving scenarios,most existing learning-based models may suffer from the so-called“catastrophic forgetting,”which refers to their inability to perform well in previously learned scenarios after acquiring new ones.As a solution to the aforementioned issue,this paper presents a framework for continual driver behaviour learning(CDBL)by leveraging continual learning technology.The proposed CDBL framework is demonstrated to outperform existing methods in behaviour prediction through a case study.Finally,future works,potential challenges and emerging trends in this area are highlighted.

关 键 词：Driver behaviours Connected vehicles Continual learning Machine learning Intelligent transportation systems 

分 类 号：U471.3[机械工程—车辆工程]