The common trend in the scientific inquiry of urban areas and their populations is to use real-
world geographic and population data to understand, explain, and predict urban …

One of the main challenges in testing autonomous driving systems is the presence of
machine learning components, such as neural networks, for which formal properties are …

The trend of connected/autonomous features adds significant complexity to the traditional
automotive systems to improve driving safety and comfort. Engineers are facing significant …

We describe an agent-based city evolution algorithm creating road networks over time, and
explore several approaches for analyzing the malleability of the algorithm to exposed …

Many recent approaches for automated driving (AD) functions currently include components
relying on deep neural networks (DNNs). One approach in order to test AD functions is the …

Virtual simulation environments are widely used to test autonomous driving software by
creating highly complex driving scenarios that are non-trivial to set up in a physical …

Automated driving systems are in an intensive research and development stage, and the
companies developing these systems are targeting to deploy them on public roads in a very …

While executing nominal tests on mobile robots is required for their validation, such tests
may overlook faults that arise under trajectories that accentuate certain aspects of the robot's …

This dissertation presents a methodology for validation of simulation models to ensure
virtual safeguarding of automated vehicles. The methodology quantifies model uncertainties …

A safety assessment as part of type approval/homologation is a legal requirement for the
market launch of automated vehicles. In Europe, homologation is carried out by a technical …