As Autonomous Vehicles (AV) are becoming a reality, the design of efficient motion control
algorithms will have to deal with the unpredictable and interactive nature of other road users …

In this paper, we present a system to train driving policies from experiences collected not just
from the ego-vehicle, but all vehicles that it observes. This system uses the behaviors of …

The actions of an autonomous vehicle on the road affect and are affected by those of other
drivers, whether overtaking, negotiating a merge, or avoiding an accident. This mutual …

This letter presents an approach for implementing game-theoretic decision-making in
combination with deep reinforcement learning to allow vehicles to make decisions at an …

In this paper, we study how to learn a harmonious deep reinforcement learning (DRL) based
lane-changing strategy for autonomous vehicles without Vehicle-to-Everything (V2X) …

Driving requires the ability to handle unpredictable situations. Since it is not always possible
to predict an impending danger, a good driver should preventively assess whether a …

Prediction of pedestrian behavior is critical for fully autonomous vehicles to drive in busy city
streets safely and efficiently. The future autonomous cars need to fit into mixed conditions …

As autonomous vehicles (AVs) become ubiquitous, users' trust will be critical for the
successful adoption of such systems. Prior works have shown that the driving styles of AVs …

The development of artificial intelligence has led researchers to study the ethical principles
that should guide machine behavior. The challenge in building machine morality based on …

Autonomous vehicles are more likely to be accepted if they drive accurately, comfortably, but
also similar to how human drivers would. This is especially true when autonomous and …