Towards a Framework to Manage Perceptual Uncertainty for Safe Automated Driving
This addresses safety concerns in autonomous driving by proposing a framework to manage uncertainty, but it is incremental as it is a position paper outlining initial steps.
The paper tackles the problem of perceptual uncertainty in autonomous vehicle perception by identifying it as a safety-critical performance measure and its influencing factors in supervised ML, aiming to develop a framework for measurement and control to support safety claims.
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.