ABSTRACT The coexistence of Connected Automated Vehicles (CAVs) and Human-driven
Vehicles (HVs) in traffic scenarios is expected to persist in the long term. Hence, proposing …

Autonomous vehicles are being developed to make road traffic safer in the future. The time
when autonomous vehicles are actually safe enough to be used in real traffic is a current …

Trajectory optimization under uncertainty underpins a wide range of applications in robotics.
However, existing methods are limited in terms of reasoning about sources of epistemic and …

Robots will increasingly operate near humans that introduce uncertainties in the motion
planning problem due to their complex nature. Optimization-based planners typically avoid …

We propose a stochastic model predictive control (SMPC) formulation for path planning with
autonomous vehicles in scenarios involving multiple agents with multimodal predictions …

Machine learning (ML) plays a crucial role in assessing traversability for autonomous rover
operations on deformable terrains but suffers from inevitable prediction errors. Especially for …

Lane-change trajectory planning of autonomous light trucks is closely related to safety,
driving stability and transportation efficiency, especially in complex road and traffic …

Managing voltage and active load in distribution networks is an increasingly challenging
task with the integration of volatile distributed energy resources (DERs) and flexible …

Current motion planning approaches for autonomous mobile robots often assume that the
low level controller of the system is able to track the planned motion with very high accuracy …

Motion planning directly in the spatiotemporal dimension can generate trajectories of higher
quality compared to decoupled methods for autonomous driving. However, it requires a …