An Efficient Approach to Simulation-Based Robust Function and Sensor Design Applied to an Automatic Emergency Braking System

Vehicular safety functions can increase automotive safety by intervening in dangerous situations. However, as such functions rely on sensor measurements to decide actions, they are subject to sensor measurement errors which influence the performance. Therefore, a manufacturer has to design both the sensors and functions in a robust manner considering these errors. A methodology for such a robust design has already been proposed for an automatic emergency braking (AEB) system and is based on a probabilistic quality measure. It is often only possible to evaluate such a probabilistic quality measure through simulations of the system under design. Therefore, a novel approach for efficiently evaluating the probabilistic quality measure through simulations of the AEB system is proposed. The structure of the stochastic problem is analyzed and the new approach derived accordingly. Numerical examples illustrate the savings in computational effort as compared to a Monte Carlo simulation and the accuracy limits. Moreover, the proposed approach generalizes to other vehicular safety systems as well.