Autonomous vehicles (AVs) are expected to reshape future transportation systems, and
decision making is one of the critical modules toward high-level automated driving. To …

Autonomous driving has rapidly developed and shown promising performance with recent
advances in hardware and deep learning methods. High-quality datasets are fundamental …

Autonomous driving has rapidly developed and shown promising performance due to recent
advances in hardware and deep learning techniques. High-quality datasets are fundamental …

Autonomous vehicles (AVs) represent a transformative advance in automotive technology,
promising increased safety and efficiency by reducing human error. However, integrating …

Driver distraction behavior recognition is currently a significant study area that involves
analyzing and identifying various movements, actions, and patterns exhibited by drivers …

Distracted driving is one of the leading causes of road accidents. With the recent introduction
of advanced driver assistance systems and L2 vehicles, the role of driver attention has …

Driver attention prediction implies the intention understanding of where the driver intends to
go and what object the driver concerned about, which commonly provides a driving task …

Distracted driving is a major cause of road fatalities. With improvements in driver (in)
attention detection, these distracted situations can be caught early to alert drivers and …

Situational awareness of the driving environment is crucial for making safe and informed
driving decisions. It can be deteriorated by distractions or environmental properties such as …

Modern Advanced Driver-Assistance Systems (ADAS) increasingly rely on interactions
between vehicle and human driver. To inform these interactions, it is helpful for a vehicle …