Autonomous vehicles (AVs) need to reason about the multimodal behavior of neighboring
agents while planning their own motion. Many existing trajectory planners seek a single …

In highly interactive driving scenarios, the actions of one agent greatly influence those of its
neighbors. Planning safe motions for autonomous vehicles (AVs) in such interactive …

Motion prediction and cost evaluation are vital components in the decision-making system of
autonomous vehicles. However, existing methods often ignore the importance of cost …

In most classical Autonomous Vehicle (AV) stacks, the prediction and planning layers are
separated, limiting the planner to react to predictions that are not informed by the planned …

Abstract In most classical Autonomous Vehicle (AV) stacks, the prediction and planning
layers are separated, limiting the planner to react to predictions that are not informed by the …

Learning-based approaches to autonomous vehicle planners have the potential to scale to
many complicated real-world driving scenarios by leveraging huge amounts of driver …

Trajectory prediction is a critical functionality of autonomous systems that share
environments with uncontrolled agents, one prominent example being self-driving vehicles …

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 …

Recently significant progress has been made in vehicle prediction and planning algorithms
for autonomous driving. However, it remains quite challenging for an autonomous vehicle to …

An intelligent agent operating in the real-world must balance achieving its goal with
maintaining the safety and comfort of not only itself, but also other participants within the …