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.

To realize the widespread application and continuous functional development of intelligent vehicles, test and evaluation of vehicle's functionality and Safety Performance in complex off-road scenarios are fundamental. Since traditional distance-based road tests cannot meet the evolving test requirements, a method to design the function-based off-road testing scenario library for intelligent vehicles(Ⅳ) is proposed in this paper. The testing scenario library is defined as a critical set of scenarios that can be used for Ⅳ tests. First, for the complex and diverse off-road scenarios, a hierarchical, structural model of the test scenario is built. Then, the critical test scenarios are selected adaptively according to the vehicle model to be tested. Next, those parameters representing the challenging test scenarios are selected. The selected parameters need to fit the natural distribution probability of scenarios. The critical test-scenario library is built combing these parameters with the structural model. Finally, the test scenarios that are most approximate to the natural driving scenario are determined with importance sampling theory. The test-scenario library built with this method can provide more critical test scenarios, and is widely applicable despite different vehicle models. Verified by simulation in the off-road interaction scenarios, test would be accelerated significantly with this method, about 800 times faster than testing in the natural road environment.