This paper proposes a two-stage trajectory planning method for self-driving vehicles (SDVs)
in intersection scenarios with uncertain social circumstances while considering other traffic …

As a core part of an autonomous driving system, motion planning plays an important role in
safe driving. However, traditional model-and rule-based methods lack the ability to learn …

Reinforcement learning (RL) has been widely used in the decision-making of autonomous
vehicles (AVs) in recent studies. However, existing RL methods generally find the optimal …

Accurate trajectory prediction of surrounding vehicles is vital for automated vehicles to
achieve high-level driving safety in complex situations. However, most state-of-the-art …

The deployment of connected, automated vehicles (CAVs) provides the opportunity to
enhance the safety and efficiency of transportation systems. However, despite the rapid …

This paper proposes an interaction and safety-aware motion-planning method for an
autonomous vehicle in uncertain multi-vehicle traffic environments. The method integrates …

To improve the safety, comfort, and efficiency of the intelligent transportation system,
particularly in complex traffic environments where autonomous vehicles (AVs) and human …

This paper presents a motion-planning framework for urban autonomous driving at
uncontrolled intersections. The intention and future state of the target vehicles are predicted …

This paper presents a novel integrated approach to deal with the decision making and
motion planning for lane-change maneuvers of autonomous vehicle (AV) considering social …

The prediction of surrounding agent trajectories in heterogeneous traffic environments
remains a challenging task for autonomous driving due to several critical issues, such as …