Interdisciplinary safety analysis of complex socio-technological systems based on the Functional Resonance Accident Model: An application to railway traffic supervision. Reliability Engineering and System Safety, Elsevier, 2011, 96, pp. AbstractThis paper presents an application of Functional Resonance Accident Models (FRAM) for the safety analysis of complex socio-technological systems, i.e. systems which include not only technological, but also human and organizational components. The supervision of certain industrial domains provides a good example of such systems, because although more and more actions for piloting installations are now automatized, there always remains a decision level (at least in the management of degraded modes) involving human behavior and organizations. The field of application of the study presented here is railway traffic supervision, using modern Automatic Train Supervision (ATS) systems. Examples taken from railway traffic supervision irrelevant information likely to distract operators).

Multi-sensor perception have an important role in robotics and autonomous systems, as inputs for critical functions such as obstacle detection, localization, etc. This Multi-sensor perception begins to appear in critical applications, such as drones and ADAS (Advanced Driver Assistance Systems). However such complex systems are dicult to validate entirely. In this paper we study these systems under an alternative dependability method: fault tolerance. We propose an approach to tolerate faults in multi-sensor data fusion based on the more classical method of duplication-comparison, and oering detection and recovery services. We detail an example implementation using Kalman lters data fusion for mobile robot localization. We demonstrate its eectiveness in this case study using real data and fault injection.

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