A Fail-Safe Decision Architecture for CCAM Applications

  • Mario Rodríguez-Arozamena*
  • , Jose Matute
  • , Joshué Pérez
  • , Burcu Ozbay
  • , Deryanur Tezcan
  • , Enes Begecarslan
  • , Irem Mutlukaya
  • , Kevin Gomez Buquerin
  • , Tina Volkersdorfer
  • , Hans Joachim Hof
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

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Abstract

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.

Original languageEnglish
Title of host publicationLecture Notes in Mobility
PublisherSpringer
Pages731-737
Number of pages7
DOIs
Publication statusPublished - 2026

Publication series

NameLecture Notes in Mobility
VolumePart F1025
ISSN (Print)2196-5544
ISSN (Electronic)2196-5552

Keywords

  • CCAM
  • Decision
  • Fail-Safe
  • Fallback Strategy
  • Situational Awareness
  • Urban Scenarios

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