Driver anomaly quantification is a fundamental capability to support human-centric driving
systems of intelligent vehicles. Existing studies usually treat it as a classification task and …

Conditionally automated vehicles can be operated on most regular roads without driver's
supervision. They show excellent potential for market adoption and are now being targeted …

Since emotions generally affect driver's behavior, judgment, and reaction time, accurately
identifying driver's emotions is of great significance to improve the safety and comfort of …

Hand gesture recognition is essential to human-computer interaction as the most natural
way of communicating. Furthermore, with the development of 3D hand pose estimation …

Traditional video-based human activity recognition has experienced remarkable progress
linked to the rise of deep learning, but this effect was slower as it comes to the downstream …

Drowsiness detection holds paramount importance in ensuring safety in workplaces or
behind the wheel, enhancing productivity, and healthcare across diverse domains …

Abnormal behavior has become the leading cause of injuries among the elderly in the
modern society. Elderly anomaly is a widespread concern in both academic and industrial …

Recognition of abnormal driving behavior is an important application area as it can support
driving reliability and improve safety. In the last decade, deep learning methods have been …

Distracted driver activity recognition plays a critical role in risk aversion-particularly
beneficial in intelligent transportation systems. However, most existing methods make use of …

Driver distraction detection not only helps to improve road safety and prevent traffic
accidents, but also promotes the development of intelligent transportation systems, which is …