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 …

Planning safe trajectories under uncertain and dynamic conditions makes the autonomous
driving problem significantly complex. Current heuristic-based algorithms such as the slot …

Autonomous vehicles must be able to understand the surrounding traffic flows and predict
the future traffic conditions for planning a safe maneuver. During prediction, the action of …

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

Trajectory prediction has received much attention recently, especially in autonomous
driving. Many Proposed models generate multi-modal trajectories using a wide variety of …

Motion planning plays a significant role in enabling advances of autonomous vehicles in
saving lives and improving traffic efficiency. In a predictive motionplanning strategy, the ego …

Trajectory generation and prediction are two in-terwoven tasks that play important roles in
planner evaluation and decision making for intelligent vehicles. Most existing methods focus …

Generating safe, smooth, and efficient trajectories is a fundamental and difficult task for
multiple unmanned ground vehicles (MUGVs) in unstructured narrow interaction scenarios …

Conventional perception-planning pipelines of autonomous vehicles (AV) utilize deep
learning (DL) techniques that typically generate deterministic outputs without explicitly …

Interactive behavior-aware planning benefits from the hierarchical learning process when
adapting to dense traffic. However, the difficulty in the Intelligent Transportation System (ITS) …