Trustworthy AI for Connected, Cooperative and Automated Mobility.
AI4CCAM leverages the potential of AI to create trustworthy and ethical models for predicting the behavior of vulnerable road users in urban environments. Its focus on user acceptance of automated vehicles and ethical dilemmas ensures the development of AI systems that people can trust.
Connected and Adaptive Maintenance for Safer Urban and Secondary Roads
CAMBER aims to develop and demonstrate improved safety monitoring across urban and secondary rural road networks by using real-time data to inform road maintenance systems and implementing cost-effective, proven interventions.
Resilient and continuous safety assurance methodology for CCAM and its HMI components
CERTAIN delivers a resilient, human-centric framework for continuous safety assurance in Connected, Cooperative and Automated Mobility (CCAM) across Europe. Placing Safety, Trust, Acceptance and Comfort (STAC) at the heart of every vehicle lifecycle stage—from design to deployment—it ensures technology evolves with people in mind.
