The last decade witnessed a great development of automated driving vehicles (ADVs) and
vehicle intelligence. The significant increment of machine intelligence poses a new …

Advances in deep learning over the last decade have led to a flurry of research in the
application of deep artificial neural networks to robotic systems, with at least 30 papers …

Driver distraction has become a significant cause of severe traffic accidents over the past
decade. Despite the growing development of vision-driven driver monitoring systems, the …

With the rapid development of intelligent vehicles, drivers are increasingly likely to share
their control authorities with the intelligent control unit. For building an efficient Advanced …

In the past twenty years, progress in intrusion detection has been steady but slow. The
biggest challenge is to detect new attacks in real time. In this work, a deep learning …

To minimize any impediments in real-time Internet of Things (IoT)-enabled robotics
applications, this study demonstrated how to build and deploy a revolutionary framework …

Intelligent vehicles and advanced driver assistance systems (ADAS) need to have proper
awareness of the traffic context, as well as the driver status since ADAS share the vehicle …

This paper highlights the role of humans in the next generation of driver assistance and
intelligent vehicles. Understanding, modeling, and predicting human agents are discussed …

Deep Neural Networks (DNNs) trained on one dataset (source domain) do not perform well
on another set of data (target domain), which is different but has similar properties as the …

Tracking drivers' eyes and gazes is a topic of great interest in the research of advanced
driving assistance systems (ADAS). It is especially a matter of serious discussion among the …