The field of autonomous systems is moving towards a greater emphasis on safety and verification, with a focus on developing innovative methods for ensuring the reliability and trustworthiness of these systems. Recent developments have centered around the use of probabilistic model checking, multi-agent reinforcement learning, and safety integrity frameworks to address the challenges posed by complex and uncertain operating environments. These approaches aim to provide guarantees on the behavior of autonomous systems, mitigate the risks associated with hardware and software faults, and optimize safety and diagnostic performance. Noteworthy papers in this area include:

• A proposal for constructing provably conservative Interval Markov Decision Process models of closed-loop systems with perception components, which has been evaluated in an automatic braking case study.
• A novel Multi-Agent Pessimistic Model-Based Reinforcement Learning framework for Connected Autonomous Vehicles, which incorporates a max-min optimization approach to enhance robustness and decision-making.
• A comprehensive safety framework for automated driving, which combines established qualitative and quantitative methods to systematically minimize risks associated with hardware and software faults.
• An optimization of safety and diagnostic performance in industrial drive systems using Uppaal Stratego, which leverages reinforcement learning to improve fault detection ability.
• A new approach for discovering unknown rare behaviors in cyber-physical systems using simulation-based testing, which develops accelerated sampling algorithms to speed up the process of finding rare mode sequences.