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 …

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

Several converging trends appear to reshape the way citizens and goods move about.
These trends are social, including urbanization and population growth, and technological …

Object detection is an important task for self-driving vehicles or advanced driver assistant
systems (ADASs). Additionally, visual selective attention is a crucial neural mechanism in a …

We present a new on-road driving dataset, called “Look Both Ways”, which contains
synchronized video of both driver faces and the forward road scene, along with ground truth …

Injuries and fatalities for vulnerable road users, especially bicyclists and pedestrians, are on
the rise. To better inform design for vulnerable road users, we need to evaluate how bicyclist …

Naturalistic driving data (NDD) can help understand drivers' reactions to each driving
scenario and provide personalized context to driving behavior. However, NDD requires a …

Traffic accident prediction in driving videos aims to provide an early warning of the accident
occurrence, and supports the decision making of safe driving systems. Previous works …

Learning the human--mobility interaction (HMI) on interactive scenes (eg, how a vehicle
turns at an intersection in response to traffic lights and other oncoming vehicles) can …

To further advance driver monitoring and assistance systems, it is important to understand
how drivers allocate their attention, in other words, where do they tend to look and why …