Motion planning for merging scenarios accounting for measurement and prediction
uncertainties is a major challenge on the way to autonomous driving. Classical methods …

Motion planning involves decision making among combinatorial maneuver variants in urban
driving. A planner must consider uncertainties and associated risks of the maneuver …

Motion planning for car-like robots is one of the major challenges in automated driving. It
requires to solve a two-point boundary value problem that connects a start and a goal …

In order to generate favorable trajectories, road users need to cope with interaction among
them, especially in dense traffic. Thus, for autonomous cars, the intention of involved …

Trajectory planning is one of the most important modules of the Autonomous Driving
Systems (ADSs), which aims to achieve a safe and comfortable interaction between the …

This paper presents an automatic merging algorithm for autonomous driving vehicles, which
decouples the specific motion planning problem into a Dual-Layer Automatic Merge …

In recent years, great progress in the area of automated and autonomous driving has been
made. Nevertheless, key areas such as situation awareness and maneuvering still remain …

This study presents a general optimal trajectory planning (GOTP) framework for autonomous
vehicles (AVs) that can effectively avoid obstacles and guide AVs to complete driving tasks …

Motion planning algorithms, an essential part of the autonomous driving system, have been
extensively studied. However, in large-scale complex scenarios, how to develop an optimal …

Motion planning for autonomous vehicles remains a challenge in urban road environments
with occlusions. In this study, we present a motion planning framework that prioritizes safety …