This paper addresses the problem of collaborative multi-agent autonomous driving of
connected and automated vehicles (CAVs) in lane-free highway scenarios. We eliminate the …

In this work we put forward a predictive trajectory planning framework to help autonomous
vehicles plan future trajectories. We develop a partially observable Markov decision process …

Safety applications at road intersections require algorithms that can estimate the manoeuvre
intention of all the drivers in the scene. In this paper, the use of contextual information …

The advent of autonomous vehicles (AVs) alongside human-driven vehicles (HVs) has
ushered in an era of mixed traffic flow, presenting a significant challenge: the intricate …

In this paper a robust control framework is proposed for the lane-keeping and obstacle
avoidance of semi-autonomous ground vehicles. A robust Model Predictive Control …

The operational space of an autonomous vehicle (AV) can be diverse and vary significantly.
Due to this, formulating a rule based decision maker for selecting driving maneuvers may …

Accurately predicting the possible behaviors of traffic participants is an essential capability
for future autonomous vehicles. The majority of current researches fix the number of driving …

The project “Autonomous Driving” at Ulm University aims at advancing highly-automated
driving with close-to-market sensors while ensuring easy exchangeability of the particular …

This paper considers the problem of optimal trajectory generation for autonomous driving
under both continuous and logical constraints. Classical approaches based on continuous …

Driving requires forecasts. Forecasted movements of objects in the driving scene are
uncertain. Inevitably, decision and control algorithms for autonomous driving need to cope …