Identification of Risk Significant Automotive Scenarios Under Hardware Failures
This work addresses safety assurance for autonomous vehicles, specifically targeting hardware failure risks, but it appears incremental as it builds on existing Markov and mapping techniques without introducing a fundamentally new approach.
The paper tackles the challenge of ensuring safety in autonomous vehicles against hardware failures by proposing a Backtracking Process Algorithm (BPA) based on Markov/Cell-to-Cell Mapping to identify critical scenarios that violate safety goals, with a case study on a brake-by-wire failure showing identification of risk-significant scenarios leading to collisions.
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.