Offline Reinforcement learning (RL) has shown potent in many safe-critical tasks in robotics
where exploration is risky and expensive. However, it still struggles to acquire skills in …

An effective understanding of the contextual environment and accurate motion forecasting of
surrounding agents is crucial for the development of autonomous vehicles and social mobile …

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

Safe Reinforcement Learning (RL) aims to find a policy that achieves high rewards while
satisfying cost constraints. When learning from scratch, safe RL agents tend to be overly …

Neural network-based driving planners have shown great promises in improving task
performance of autonomous driving. However, it is critical and yet very challenging to ensure …

This research proposes an integrated behavioral and motion planner with proactive right-of-
way acquisition capability. The proposed planner overcomes the shortcomings of …

Connectivity technology has shown great potentials in improving the safety and efficiency of
transportation systems by providing information beyond the perception and prediction …

Uncontrolled intersections with interaction and uncertainties are challenging for autonomous
vehicles (AV) to manage. In this work, we propose a decision-making model specific to …

Uncontrolled intersections are important and challenging traffic scenarios for autonomous
vehicles. Vehicles not only need to avoid collisions with dynamic vehicles instantaneously …

This paper advances state-of-the-art automated driving systems with a comprehensive
framework that encompasses decision making, maneuver planning, and trajectory tracking …