Continual driver behaviour learning for connected vehicles and intelligent transportation systems

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

Li, Zirui (Technische Universität Dresden; Beijing Institute of Technology)
Gong, Cheng (Beijing Institute of Technology)
Lin, Yunlong (Beijing Institute of Technology)
Li, G. (TU Delft Transport and Planning)
Wang, Xinwei (Queen Mary University of London)
Lu, Chao (Beijing Institute of Technology)
Wang, Miao (Baidu, Inc.)
Chen, Shanzhi (China Information and Communication Technology Group Co., Ltd.)
Gong, Jianwei (Beijing Institute of Technology)

2023

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.

Connected vehicles
Continual learning
Driver behaviours
Intelligent transportation systems
Machine learning

http://resolver.tudelft.nl/uuid:e87b6981-743e-42f4-a2a3-cf658f685ebc

https://doi.org/10.1016/j.geits.2023.100103

Green Energy and Intelligent Transportation, 2 (4)

Institutional Repository

review

© 2023 Zirui Li, Cheng Gong, Yunlong Lin, G. Li, Xinwei Wang, Chao Lu, Miao Wang, Shanzhi Chen, Jianwei Gong