Autonomous vehicles need to plan at the task level to compute a sequence of symbolic
actions, such as merging left and turning right, to fulfill people's service requests, where …

Autonomous vehicles need to plan at the task level to compute a sequence of symbolic
actions, such as merging left and turning right, to fulfill people's service requests, where …

Motion planning for autonomous vehicles in complex, real-world urban scenarios is a
fundamental challenge in autonomous driving. To this end, we present MIMP, a Modular and …

Motion planning is essential for an autonomous vehicle to perform safe and human-like
driving behaviors, especially in highly dynamic scenarios such as dense urban and highway …

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 …

The motion planning problem in autonomous driving is becoming increasingly important as
more and more autonomous vehicles hit real roads. Motion planning is also usually …

Validating motion planning algorithms for autonomous vehicles on a real system is essential
to improve their safety in the real world. Open-source initiatives, such as Autoware, provide a …

This paper presents an efficient hierarchical motion planning framework with a long
planning horizon for autonomous driving in structured environments. A 3D motion planning …

This paper presents preliminary work on learning the search heuristic for the optimal motion
planning for automated driving in urban traffic. Previous work considered search-based …

Efficient trajectory planning for urban intersections is currently one of the most challenging
tasks for an Autonomous Vehicle (AV). Courteous behavior towards other traffic participants …