The field of autonomous vehicles is witnessing significant developments in safety assurance and system design. Researchers are focusing on creating rigorous statistical foundations for scenario-based testing, enabling the quantification of risk and evaluation of testing effectiveness. Additionally, there is a growing emphasis on domain-specific languages and quantitative types to facilitate semantically coherent system design and assurance cases. These advancements aim to address the challenges posed by the complexity and uncertainty of autonomous vehicle development, ensuring the safe and efficient operation of connected and automated vehicles. Noteworthy papers include:

• Certus, which introduces a domain-specific language for quantitative confidence assessment in assurance cases, allowing users to represent their judgment using vague but linguistically meaningful terminology.
• On the Need for a Statistical Foundation in Scenario-Based Testing of Autonomous Vehicles, which proposes proof-of-concept models to quantify the probability of failure per scenario and evaluate testing effectiveness under varying conditions.