Autonomous robots are increasingly served in real-world unstructured human environments
with complex long-horizon tasks, such as restaurant serving and office delivery. Task and …

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 …

To enable autonomous vehicles to generate smooth and collision-free trajectories and
improve their driving performance on structured roads, this paper proposes a hierarchical …

In autonomous driving, an accurate understanding of the environment, eg, the vehicle-to-
vehicle and vehicle-to-lane interactions, plays a critical role in many driving tasks, such as …

Motion planning is an essential element of the modular architecture of autonomous vehicles,
serving as a bridge between upstream perception modules and downstream low-level …

This study explores the integration of dynamic vehicle trajectories, vehicle safety factors,
static traffic environments, and actuator constraints to improve cooperative intent modeling …

As one of the fundamental tasks of autonomous driving, depth perception aims to perceive
physical objects in three dimensions and to judge their distances away from the ego vehicle …

For autonomous vehicles, transfer learning can enhance performance by making better use
of previously learned knowledge in newly encountered scenarios, which holds great …

This paper discusses a new issue named domain generalization of fast motion planning in
3D environments, which benefits agility-required robot applications such as autonomous …

Trajectory planning in mixed traffic scenarios is a critical and demanding task in autonomous
driving, since it necessitates addressing intricate interactions among traffic agents while …