Pedestrian trajectory prediction is a classic and challenging scientific task that involves
complex engineering science and human factors. These challenges have spurred a …

Driver behavior modeling is an important task for predicting or simulating the evolution of
traffic situations. We investigate the use of Adversarial Inverse Reinforcement Learning …

This paper introduces a procedure for controlling autonomous vehicles entering
roundabouts. The aim of the centralized controller is to define the velocity profile of each …

This research investigates the utilization of human driving models in autonomous vehicles,
particularly in scenarios with minimal or no interactions with other vehicles. Human driving …

Predicting other drivers' trajectories is challenging. We address the issue by introducing a
method to derive a driving policy based on multi-agent reinforcement learning. For this, we …

A digital twin is a digital replica of a physical entity in the real world. As an emerging
technology, it has been widely used in various industries. Vehicle-to-everything (V2X) …

The transportation sector accounts for the most greenhouse gas emissions among all
economic sectors. Thus, making our transportation systems sustainable is crucial for …

Im automatisierten Fahren arbeiten verschiedene Algorithmen zusammen, die Informationen
übergeben, z. B. von der Wahrnehmung an die Planung. Dabei können sich Unsicherheiten …

This dissertation explores the application of uncertainty-aware planning in the context of
automated driving. As automated driving technology continues to evolve, it is crucial to …