Driver attention prediction is becoming an essential research problem in human-like driving
systems. This work makes an attempt to predict the d river a ttention in d riving a ccident …

Driver attention prediction is currently becoming the focus in safe driving research
community, such as the DR (eye) VE project and newly emerged Berkeley DeepDrive …

In recent years, studying how drivers allocate their attention while driving is critical in
achieving human-like cognitive ability for autonomous vehicles. And it has been an active …

Driver attention modeling is a crucial technique in building human-centric intelligent driving
systems. Considering the human visual mechanism, this study leverages multi-level visual …

Robust driver attention prediction for critical situations is a challenging computer vision
problem, yet essential for autonomous driving. Because critical driving moments are so rare …

The traffic driving environment is a complex and dynamic changing scene in which drivers
have to pay close attention to salient and important targets or regions for safe driving …

Building a multi-modality multi-task neural network toward accurate and robust performance
is a de-facto standard in perception task of autonomous driving. However, leveraging such …

Traffic accident anticipation is a vital function of Automated Driving Systems (ADS) in
providing a safety-guaranteed driving experience. An accident anticipation model aims to …

Automated driving desires better performance on tasks like motion planning and interacting
with pedestrians in mixed-traffic environments. Deep learning algorithms can achieve high …

Advanced Driver Assistance Systems (ADAS) have made driving safer over the last decade.
They prepare vehicles for unsafe road conditions and alert drivers if they perform a …