Continual driver behaviour learning for connected vehicles and intelligent transportation systems
Framework, survey and challenges
Zirui Li (Beijing Institute of Technology, Technische Universität Dresden)
Cheng Gong (Beijing Institute of Technology)
Yunlong Lin (Beijing Institute of Technology)
G. Li (TU Delft - Transport and Planning)
Xinwei Wang (Queen Mary University of London)
Chao Lu (Beijing Institute of Technology)
Miao Wang (Baidu, Inc.)
Shanzhi Chen (China Information and Communication Technology Group Co., Ltd.)
Jianwei Gong (Beijing Institute of Technology)
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Abstract
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.
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