A Fail-Safe Decision Architecture for CCAM Applications

by Mario Rodríguez-Arozamena, Jose Matute, Joshué Pérez (TECNALIA Research & Innovation), Burcu Ozbay, Deryanur Tezcan, Enes Begecarslan, Irem Mutlukaya (FEV Türkiye), Kevin Gomez Buquerin, Tina Volkersdorfer and Hans-Joachim Hof (CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS), Technische Hochschule Ingolstadt)

In the context of Connected, Cooperative, and Automated Mobility (CCAM), precise ego-vehicle positioning and environmental status assessment are crucial. However, these tasks can be susceptible to sensor failures, misuse, and cyberattacks. Automation disengagements and system redundancy are common strategies to achieve Minimum Risk Conditions when failures occur. This paper presents a Fail-Safe decision architecture formulated within the framework of the SELFY project (https://selfy-project.eu/ ). The main aim is to reduce inaccuracies in GNSS-derived positioning through the incorporation of sensor fusion, AI-guided situational assessment, trajectory planning, and mode decision components. Additionally, the architecture has been designed to enable real-time updates and communication with external entities, including the Vehicle Security Operations Centre.