Safety and Sustainable Development of Automated Driving in Mixed-Traffic Urban Areas—Considering Vulnerable Road Users and Network Efficiency

Next to environmental aspects, establishing areas for safe and economically viable automated driving in mixed-traffic settings is one major challenge for sustainable development of Autonomous Vehicles (AVs). This work investigates safety in the interactions between AVs, human-driven vehicles, and vulnerable road users, including cyclists and...

Should I Stop or Should I Cross?: Interactions between vulnerable road users and automated vehicles

This dissertation aims to understand the behavior of pedestrians and cyclists when interacting with automated vehicles (AVs). The role of AVs’ characteristics such as their physical appearance, whether a driver is present, the presence of external communication interfaces, and factors pertaining to the behavior of the vehicle were investigated...

Crafted vs. Learned Representations in Predictive Models - A Case Study on Cyclist Path Prediction

This paper compares two models for context-based path prediction of objects with switching dynamics: a Dynamic Bayesian Network (DBN) and a Recurrent Neural Network (RNN). These models are instances of two larger model categories, distinguished by whether expert knowledge is explicitly crafted into the state representation (and thus is...