Connected and automated vehicles (CAVs) are supposed to share the road with human-
driven vehicles (HDVs) in a foreseeable future. Therefore, considering the mixed traffic …

In this work we put forward a predictive trajectory planning framework to help autonomous
vehicles plan future trajectories. We develop a partially observable Markov decision process …

The present study proposes a decision-making model based on different models of driver
behavior, aiming to ensure integration between road safety and crash reduction based on …

Real-time safety prediction models are vital in proactive road safety management strategies.
This study develops models to predict traffic conflicts at signalized intersections at the signal …

Driving aggression is a major concern during car-following situations as it is closely
associated with collision risk and crash severity. Despite the tendency of reckless driving …

Telematics data, primarily collected from on-board vehicle devices (OBDs), has been utilised
in this study to generate a thorough understanding of driving behaviour. The urban case …

The application of naturalistic driving data (NDD) has the potential to answer critical
research questions in the area of driving behavior assessment, as well as the impact of …

Identifying critical safety management drivers with high driver-level risks is essential for
traffic safety improvement. Previous studies commonly evaluated driver-level risks based …

Driver intention recognition is a critical component of advanced driver assistance systems,
with significant implications for improving vehicle safety, intelligence, and fuel economy …

In the foreseeable future, electric vehicles (EVs) will play a key role in the decarbonization of
transport systems. Replacing vehicles powered by internal combustion engines (ICEs) with …