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 …

Motion control algorithms in the presence of pedestrians are critical for the development of
safe and reliable Autonomous Vehicles (AVs). Traditional motion control algorithms rely on …

Motion planning under uncertainty is one of the main challenges in developing autonomous
driving vehicles. In this work, we focus on the uncertainty in sensing and perception, resulted …

For autonomous vehicles integrating onto road-ways with human traffic participants, it
requires understanding and adapting to the participants' intention by responding in …

As more and more autonomous vehicles (AVs) are being deployed on public roads,
designing socially compatible behaviors for them is becoming increasingly important. In …

Although deep reinforcement learning (DRL) methods are promising for making behavioral
decisions in autonomous vehicles (AVs), their low training efficiency and difficulty to adapt to …

With the integration of Autonomous Vehicles (AVs) into our transportation systems, their
harmonious coexistence with Human-driven Vehicles (HVs) in mixed traffic settings …

In the car-following scenarios, automated vehicles (AVs) usually plan motions without
considering the impacts of their actions on the following human drivers. This letter aims to …

Autonomous vehicles are sharing the road with human drivers. In order to facilitate
interactive driving and cooperative behavior in dense traffic, a thorough understanding and …

In this paper, a novel approach is presented for modeling the interaction dynamics between
an ego car and a bicycle in a traffic scenario using a hybrid reinforcement learning …