Project description
Predicting chemical and accident risks in automotive industry
Despite strict regulations, the automotive industry faces risks from workplace accidents and chemical exposures, as conventional safety systems rely largely on retrospective analysis that cannot adequately anticipate hazards in complex and rapidly changing industrial environments. Supported by the Marie Skłodowska-Curie Actions programme, the SAFE-AUTO project aims to construct a decision-support system. This system will be based on AI and machine learning, and it will predict chemical exposures and accident risks before they arise. It will use existing safety records with qualitative data from workers to build transparent, explainable predictive models that identify emerging risk patterns in dynamic industrial environments. The framework will align with EU and ISO safety standards to support safer, more sustainable and compliant manufacturing practices.
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.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- engineering and technology mechanical engineering vehicle engineering automotive engineering
- natural sciences earth and related environmental sciences environmental sciences pollution
- social sciences economics and business economics sustainable economy
- natural sciences computer and information sciences artificial intelligence machine learning
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2025-PF
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
708 00 OSTRAVA PORUBA
Czechia
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.