How can humans remain in control of artificial intelligence (AI)-based systems designed to
perform tasks autonomously? Such systems are increasingly ubiquitous, creating benefits …

Objective We aim to bridge the gap between naturalistic studies of driver behavior and
modern cognitive and neuroscientific accounts of decision making by modeling the cognitive …

A major challenge for autonomous vehicles is handling interactions with human-driven
vehicles—for example, in highway merging. A better understanding and computational …

When humans share space in road traffic, as drivers or as vulnerable road users, they draw
on their full range of communicative and interactive capabilities. Much remains unknown …

Understanding behavior of human drivers in interactions with automated vehicles (AV) can
aid the development of future AVs. Existing investigations of such behavior have …

Autonomous vehicles use a variety of sensors and machine-learned models to predict the
behavior of surrounding road users. Most of the machine-learned models in the literature …

Traffic interactions between merging and highway vehicles are a major topic of research,
yielding many empirical studies and models of driver behaviour. Most of these studies on …

Laboratory studies of abstract, highly controlled tasks point towards noisy evidence
accumulation as a key mechanism governing decision making. Yet it is unclear whether the …

Interaction between road users is a societally important special case of human interaction,
and a better understanding of such interactions is a key missing enabler for wide …

Driving automation holds significant potential for enhancing traffic safety. However,
effectively handling interactions with human drivers in mixed traffic remains a challenging …