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

Automated vehicles require efficient and safe planning to maneuver in uncertain
environments. Largely this uncertainty is caused by other traffic participants, eg, surrounding …

When driving in urban environments, an autonomous vehicle must account for the
interaction with other traffic participants. It must reason about their future behavior, how its …

Safe trajectory planning for high-performance automated vehicles in an environment with
both static and moving obstacles is a challenging problem. Part of the challenge is …

Developing safe automated vehicles that can be proactive, safe, and comfortable in mixed
traffic requires improved planning methods that are risk-averse and that account for …

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

In automated driving, risk describes potential harm to passengers of an autonomous vehicle
(AV) and other road users. Recent studies suggest that human-like driving behavior …

Trajectory planning for autonomous driving is challenging because the unknown future
motion of traffic participants must be accounted for, yielding large uncertainty. Stochastic …

Autonomous vehicles (AVs) must share the driving space with other drivers and often
employ conservative motion planning strategies to ensure safety. These conservative …

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