Identification of Risk Significant Automotive Scenarios Under Hardware Failures

Mohammad Hejase
(The Ohio State University)
Arda Kurt
(The Ohio State University)
Tunc Aldemir
(The Ohio State University)
Umit Ozguner
(The Ohio State University)

The level of autonomous functions in vehicular control systems has been on a steady rise. This rise makes it more challenging for control system engineers to ensure a high level of safety, especially against unexpected failures such as stochastic hardware failures. A generic Backtracking Process Algorithm (BPA) based on a deductive implementation of the Markov/Cell-to-Cell Mapping technique is proposed for the identification of critical scenarios leading to the violation of safety goals. A discretized state-space representation of the system allows tracing of fault propagation throughout the system, and the quantification of probabilistic system evolution in time. A case study of a Hybrid State Control System for an autonomous vehicle prone to a brake-by-wire failure is constructed. The hazard of interest is collision with a stationary vehicle. The BPA is implemented to identify the risk significant scenarios leading to the hazard of interest.

In Mario Gleirscher, Stefan Kugele and Sven Linker: Proceedings 2nd International Workshop on Safe Control of Autonomous Vehicles (SCAV 2018), Porto, Portugal, 10th April 2018, Electronic Proceedings in Theoretical Computer Science 269, pp. 59–73.
Published: 10th April 2018.

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