Abstract
Among the strong trends that are impacting society, autonomous driving stands out clearly as one of the prime candidates to cause disruptive changes in automotive industry. Fully automated driving is identified as a major enabler for mastering the grand societal challenges of safe, clean, and efficient mobility. A major probation for highly automated driving is the step change from partial to conditional automation and above. At these high levels of automation, the driver is unable to intervene in a timely and appropriate manner. Consequently, the automation must be capable of independently handling safety-critical situations. Fail-operational behavior is essential at all layers of automated driving. These layers include sensing, computation and vehicle architecture. The PRYSTINE project targets realization of Fail-operational Urban Surround perceptION (FUSION), based on robust Radar and LiDAR sensor fusion, and control functions enabling safe automated driving. PRYSTINE addresses development and validation of new fail operational platforms, as well as high performing and dependable sensor fusion on different levels. In this paper, an overview of fail-operational approaches on different layers (vehicle and sensor level) is provided, together with a description of the interplay between safety and security aspects. It is further enhanced with description of a fail-operational sensor-fusion framework on component and system level.
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Acknowledgment
This project has received funding from the Electronic Component Systems for European Leadership Joint Undertaking (ECSEL-JU) under grant agreement No 783190 (PRYSTINE Project).
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Macher, G., Druml, N., Veledar, O., Reckenzaun, J. (2019). Safety and Security Aspects of Fail-Operational Urban Surround perceptION (FUSION). In: Papadopoulos, Y., Aslansefat, K., Katsaros, P., Bozzano, M. (eds) Model-Based Safety and Assessment. IMBSA 2019. Lecture Notes in Computer Science(), vol 11842. Springer, Cham. https://doi.org/10.1007/978-3-030-32872-6_19
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