What was the challenge?
Previously, prevailing safety standards and assurance methods were tailored to systems featuring human intervention capabilities, rather than autonomous systems equipped with predefined responses. Furthermore, these methodologies assumed that once deployed, systems would remain static and not undergo learning or evolution. Nonetheless, advancements in machine learning have empowered autonomous systems to learn from errors and enhance safety. Despite these advancements, the integration of machine learning introduces an element of uncertainty in future decision-making, thereby presenting a formidable challenge for ensuring safety. Effectively addressing this challenge demanded a workforce possessing both high-level skills and a multidisciplinary approach.
Why is it important for society?
Autonomous systems have the potential to significantly reduce accidents, save lives, and prevent injuries on roads, in factories, and in other environments. However, societal acceptance of autonomous systems relies on trust. The public's perception of these technologies directly affects their adoption rates. Gaining trust through transparent safety measures, thorough validation & verification, and effective communication can facilitate the integration of autonomous systems into various sectors, from transportation to healthcare, thereby redefining industries for the better. In conclusion, building trust in the safety of autonomous systems is a cornerstone of societal progress. By assuring their reliability, society can unlock their full potential, improve safety, stimulate economic growth, and foster harmonious human-machine interaction.
What were the overall objectives?
SAS, the European Training Network for Safer Autonomous Systems, was a key instrument for getting people to trust autonomous systems by making the systems safer. In order to achieve this objective, a group of 15 highly skilled early-stage researchers investigated new forms of safety-assurance strategies, dynamic risk mitigation, fault-tolerant and failsafe hardware/software design, model-based safety analysis, as well as legal aspects related to autonomous systems.
