Driving safety has been a concern since the first cars appeared on the streets. Driver
inattention has been singled out as a major cause of accidents early on. This is hardly …

Considering the functionality of situational awareness in safety-critical automation systems,
the perception of risk in driving scenes and its explainability is of particular importance for …

Emergency prediction and driver attention prediction are fundamental tasks within the realm
of self-driving vehicles and assistant driving systems. The utilization of visual saliency …

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 …

The ability to sense and understanding the driving environment is a key technology for
ADAS and autonomous driving. Human drivers have to pay more visual attention to …

During the operation of the L3 automated driving system, since there is no need to supervise
the vehicle at all times, the driver is often disengaged from the driving task and engages in a …

A significant amount of people die in road accidents due to driver errors. To reduce fatalities,
developing intelligent driving systems assisting drivers to identify potential risks is in an …

On-road behavior analysis is a crucial and challenging problem in the autonomous driving
vision-based area. Several endeavors have been proposed to deal with different related …

In recent years, predicting driver's focus of attention has been a very active area of research
in the autonomous driving community. Unfortunately, existing state-of-the-art techniques …

Detecting dangerous traffic agents in videos captured by vehicle-mounted dashboard
cameras (dashcams) is essential to ensure safe navigation in complex environments …