As vehicle automation advances, motion planning algorithms face escalating challenges in
achieving safe and efficient navigation. Existing Advanced Driver Assistance Systems …

As vehicle automation advances, motion planning algorithms face escalating challenges in
achieving safe and efficient navigation. Existing Advanced Driver Assistance Systems …

Autonomous vehicles face numerous challenges to ensure safe operation in unpredictable
and hazardous conditions. The autonomous driving environment is characterized by high …

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 …

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 …

Motion planners for autonomous vehicles must obtain feasible trajectories in real-time
regardless of the complexity of traffic conditions. Planning approaches that discretize the …

Autonomous vehicles interacting with other traffic participants heavily rely on the perception
and prediction of other agents' behaviors to plan safe trajectories. However, as occlusions …

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 …

Recent road trials have shown that guaranteeing the safety of driving decisions is essential
for the wider adoption of autonomous vehicle technology. One promising direction is to pose …

Motion planning algorithms for urban automated driving must handle uncertainty due to
unknown intention and future motion of Dynamic Obstacles (DOs). Considering a single …