Autonomous robotic systems are complex, hybrid, and often safety critical; this makes their
formal specification and verification uniquely challenging. Though commonly used, testing …

We provide a review of recent work on formal methods for autonomous driving. Formal
methods have been traditionally used to specify and verify the behavior of computer …

Machine learning techniques have been shown to outperform many rule-based systems for
the decision-making of autonomous vehicles. However, applying machine learning is …

Intersections are difficult to navigate for both human drivers and autonomous vehicles
because several diverse traffic rules must be considered. In addition, current traffic rules are …

Safe motion planning for autonomous vehicles is a challenging task, since the exact future
motion of other traffic participant is usually unknown. In this article, we present a verification …

Automated and autonomous driving systems (ADS) are a transformational technology in the
mobility sector. Current practice for testing ADS uses virtual tests in computer simulations; …

To allow autonomous vehicles to safely participate in traffic and to avoid liability claims for
car manufacturers, autonomous vehicles must obey traffic rules. However, current traffic …

Artificial Intelligence (AI) can enable the development of next-generation autonomous safety-
critical systems in which Machine Learning (ML) algorithms learn optimized and safe …

The ethics of autonomous vehicles (AV) has received a great amount of attention in recent
years, specifically in regard to their decisional policies in accident situations in which human …

Autonomous vehicles need to be designed to abide by the same rules that humans follow.
This is challenging, because traffic rules are fuzzy and not well defined, making them …