Traffic crashes remain a leading cause of accidental human death where aggressive driving
is a significant contributing factor. To review the driver's performance presented in …

Trust calibration poses a significant challenge in the interaction between drivers and
automated vehicles (AVs) in the context of human-automation collaboration. To effectively …

Trust in automation is crucial for the safe and appropriate adoption of automated driving
technology. Current research methods to measure trust mainly rely on subjective scales …

Self-driving vehicles promise many safety, mobility, and environmental benefits. However,
users' lack of trust and acceptance may threaten the success and potential of this …

Connected and automated vehicles are vulnerable to cyber-attacks, which may jeopardise
their safe and efficient operation and, as a result, negatively affect drivers' behaviour. A …

Drivers are allowed to perform a nondriving-related task (NDRT) in the Level-3 (L3)
automated driving, which inevitably promotes a variety of complex NDRTs. Hence …

In recent years, emotion recognition has received significant attention, presenting a plethora
of opportunities for application in diverse fields such as human–computer interaction …

Early warning of driving risks can effectively prevent collisions. However, numerous studies
that predicted driving risks have suffered from the use of single data sources, insufficiently …

Fatigue driving is a significant cause of road traffic accidents and associated casualties.
Automatic assessment of driver drowsiness by monitoring electroencephalography (EEG) …

Driving risk prediction is important for the development of intelligent vehicles (IVs), and the
rise of human-like driving requires a driving risk prediction system to match the subjective …