Connected automated driving has the potential to significantly improve urban traffic
efficiency, eg, by alleviating issues due to occlusion. Cooperative behavior planning can be …

Reinforcement learning has received high research interest for developing planning
approaches in automated driving. Most prior works consider the end-to-end planning task …

Graph networks have recently been used for decision making in automated driving tasks for
their ability to capture a variable number of traffic participants. Current high-level graph …

This paper introduces a novel centralized autonomous inclusive intersection management
mechanism (CAI 2 M 2) for heterogeneous connected vehicles (HCVs). The system …

Autonomous intersection management (AIM) poses significant challenges due to the
intricate nature of real-world traffic scenarios and the need for a highly expensive centralised …

Training reinforcement learning (RL) agents for motion planning in heavily constrained
solution spaces may require extensive exploration, leading to long training times. In …

Connected automated driving promises a significant improvement of traffic efficiency and
safety on highways and in urban areas. Apart from sharing of awareness and perception …

Neural networks in the automotive sector commonly have to process varying number of
objects per observation. Deep Set feature extractors have shown great success on problems …

Autonomous intersection management (AIM) presents significant challenges due to the
complexity of real-world traffic scenarios and the reliance on a costly centralized server to …

Das vernetzte automatisierte Fahren hat das Potential, die Verkehrseffizienz im
innerstädtischen Raum signifikant zu steigern. So können Verdeckungseffekte, wie sie …