Deep learning has been widely utilized in intelligent vehicle systems, particularly in the field
of driver distraction detection. However, existing methods in this application tend to focus …

Accurate prediction of vehicle trajectories is vital for advanced driver assistance systems and
autonomous vehicles. Existing methods mainly rely on generic trajectory predictions derived …

Driver intention prediction has the potential to greatly improve the ability of autonomous
vehicles (AVs) to effectively handle risky driving behaviors, thereby ensuring driving safety …

Despite numerous studies on trajectory prediction, existing approaches often fail to
adequately capture the multifaceted and individual nature of driving behavior. In recognition …

Next-generation advanced driver-assistance systems (ADASs) are a promising direction for
intelligent transportation systems. To achieve intelligent security monitoring, it is imperative …

Prior works have addressed the problem of driver intention prediction (DIP) by identifying
maneuvers after their onset. On the other hand, early anticipation is equally important in …

Driver intention recognition (DIR) methods mostly rely on deep neural networks (DNNs). To
use DNNs in a safety-critical real-world environment it is essential to quantify how confident …

Traffic fatalities remain among the leading death causes worldwide. To reduce this figure,
car safety is listed as one of the most important factors. To actively support human drivers, it …