Abstract
Hazard analysis is the core of numerous approaches to safety engineering, including the functional safety standard ISO-26262 (FuSa) and Safety of the Intended Function (SOTIF) ISO/PAS 21448. We focus on addressing the immense challenge associated with the scope of training and testing for rare hazard for autonomous drivers, leading to the need to train and test on the equivalent of >108 naturalistic miles. We show how risk can be estimated and bounded using the probabilistic hazard analysis. We illustrate the definition of hazards using well-established tests for hazard identification. We introduce a dynamic hazard approach, whereby autonomous drivers continuously monitor for potential and developing hazard, and estimate their time to materialization (TTM). We describe systematic TTM modeling of the various hazard types, including environment-specific perception limitations. Finally, we show how to enable accelerated development and testing by training a neural network sampler to generate scenarios in which the frequency of rare hazards is increased by orders of magnitude.
Analysis of Hazards for Autonomous Driving
