Automated vehicles require the ability to cooper-ate with humans for smooth integration into
today's traffic. While the concept of cooperation is well known, developing a robust and …

Future automated vehicles must conquer the challenge of anticipating the intentions of other
traffic participants in order to ensure the safety and efficiency of road traffic. Therefore, the …

Human drivers use nonverbal communication and anticipation of other drivers' actions to
master conflicts occurring in everyday driving situations. Without a high penetration of …

Urban traffic scenarios often require a high degree of cooperation between traffic
participants to ensure safety and efficiency. Observing the behavior of others, humans infer …

Efficient driving in urban traffic scenarios requires foresight. The observation of other traffic
participants and the inference of their possible next actions depending on the own action is …

Automated driving requires decision making in dynamic and uncertain environments. The
uncertainty from the prediction originates from the noisy sensor data and from the fact that …

Today's automated vehicles lack the ability to cooperate implicitly with others. This work
presents a Monte Carlo Tree Search (MCTS) based approach for decentralized cooperative …

Automated driving requires decision making in dynamic and uncertain environments. The
uncertainty from the prediction originates from the noisy sensor data and from the fact that …

The growing demand for unmanned ground vehicles (UGVs) in various applications
necessitates the development of efficient trajectory planning techniques that can ensure …

Optimal multi-modal journey planning under uncertainty is a challenging problem, due in
part to an increased branching factor generated by nondeterministic actions. Deterministic …