Identification of Risk Significant Automotive Scenarios Under Hardware Failures

TL;DR

This paper introduces a Backtracking Process Algorithm using Markov/Cell-to-Cell Mapping to identify critical failure scenarios in autonomous vehicle control systems, enhancing safety analysis against hardware failures.

Contribution

It presents a novel algorithm for systematically identifying risk significant scenarios in autonomous vehicle systems under hardware failures.

Findings

Successfully identified critical failure scenarios leading to collisions.

Demonstrated the effectiveness of BPA in complex hybrid control systems.

Provided a framework for probabilistic safety assessment in autonomous vehicles.

Abstract

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.