Nowadays, self-driving cars are being applied to more complex urban scenarios including
intersections, merging ramps or lane changes. It is, therefore, important for self-driving cars …

It is essential for autonomous driving cars to understand and predict other surrounding cars'
behaviors, especially in urban environments, due to the high traffic volumes and complex …

With the development of self-driving technology, the fundamental behaviors like car-
following, lane change have been validated and tested in various kinds of scenarios …

Self-driving vehicles are being increasingly deployed in the wild. One of the most important
next hurdles for autonomous driving is how such vehicles will optimally interact with one …

With the development of autonomous vehicles, advanced driver-assistance systems, and
vehicular social networks, the requirement for vehicle trajectory prediction in lane changing …

In this article, the authors present a novel method to learn the personalized tactic of
discretionary lane-change initiation for fully autonomous vehicles through human-computer …

End-to-end learning and multi-sensor fusion-based methods are two major frameworks used
for self-driving cars. To enable these intelligence vehicles to acquire driving skills at a level …

Autonomous vehicles need to face human-driving vehicles with their uncertain intentions in
dynamic urban environment. Thus it leads to a challenging decision-making problem. In this …

This paper studies the decision making problem of autonomous vehicles in traffic. We model
the interaction between an autonomous vehicle and the environment as a stochastic Markov …

It is crucial to understand the surrounding cars with respect to the road context and interact
with them harmoniously for the success of autonomous cars used in the mixed urban traffic …