Analytical derivation of performance bounds of autonomous emergency brake systems

Abstract

Autonomous emergency brake (AEB) systems have to decide on brake interventions based on an uncertain and incomplete perception of the environment. This paper analyses theoretical limitations in AEB systems caused by noisy sensor measurements and uncertain prediction models. Such performance bounds can be used to derive sensor accuracy constraints, to identify challenging scenarios or to develop objective metrics. In contrast to most previous studies, this work focusses on analytical derivations. To this end, the Cramér-Rao bound of the best attainable state estimation covariance is derived from a model of sensor measurement errors. This state- and time-dependent covariance is then propagated to an AEB decision making logic that is based on a criticality measure. Additional inherent prediction uncertainty in this risk assessment is taken into account. The effectiveness of the AEB subject to uncertainties is compared to the deterministic baseline case in terms of the brake activation time and the collision energy reduction.