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 …

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

Testing and verification is still an open issue on the way to fully automated driving.
Simulations can help to reduce the required testing efforts, however, classical simulators …

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

While automated research vehicles are already populating the roads, their commercial
availability at scale is still to come. Presumably, one of the key challenges is to derive …

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

Sampling-based motion planning (SBMP) is a major algorithmic trajectory planning
approach in autonomous driving given its high efficiency and outstanding performance in …

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 …

In motion planning for automated vehicles, a thorough uncertainty consideration is crucial to
facilitate safe and convenient driving behavior. This work presents three motion planning …

Trajectory planning in the intersection is a challenging problem due to the strong uncertain
intentions of surrounding agents. Conventional methods may fail in some corner cases …