Towards a Framework to Manage Perceptual Uncertainty for Safe Automated Driving

Abstract

Perception is a safety-critical function of autonomous vehicles and machine learning (ML) plays a key role in its implementation. This position paper identifies (1) perceptual uncertainty as a performance measure used to define safety requirements and (2) its influence factors when using supervised ML. This work is a first step towards a framework for measuring and controling the effects of these factors and supplying evidence to support claims about perceptual uncertainty.

References in corpus (1)

• Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

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