SAFE-AUTO: Anticipating Chemical Hazards and Accident Risks in the Automotive Industry

Objective

Accidents and chemical risks in the automotive industry remain a major challenge, despite strict regulations and standards. Traditional occupational health and safety (OHS) systems often rely on retrospective analysis and static procedures, which are not sufficient to address emerging risks in complex and dynamic industrial environments. SAFE-AUTO project will develop an innovative, ML/AI-driven decision-support framework to strengthen workplace safety in the automotive sector. By combining existing OHS records, incident reports, and risk assessments with new qualitative evidence from interviews, surveys, and focus groups, the project will create predictive models that identify patterns and anticipate chemical exposures and accident scenarios specific to automotive workplaces before they occur. Depending on the research need, machine learning or other AI methods, combined with explainable modelling and simulations, will ensure reliability, transparency, and fairness of the results. The framework will be validated with industrial stakeholders and aligned with ISO 45001 and EU safety guidelines. This proactive approach will support compliance with EU directives and promote safer, more sustainable industrial practices. SAFE-AUTO will deliver impacts at several levels. Scientifically, it advances the use of ML/AI in occupational safety. Practically, it reduces accidents, chemical exposures, and production losses. Societally, it strengthens Europe’s commitment to a safe and healthy working environment, consistent with the EU Strategic Framework on Health and Safety at Work 2021–2027 and indirectly supporting the European Green Deal by reducing waste, pollution, and resource losses linked to industrial accidents. Overall, SAFE-AUTO offers a scalable and transferable methodology to anticipate risks, protect workers, and cut the environmental and economic costs of industrial accidents across Europe.