Robust Function and Sensor Design Considering Sensor Measurement Errors Applied to Automatic Emergency Steering

Abstract

Vehicular safety functions that take over the control during dangerous driving situations increase automotive safety. As such functions use the measurements of sensors in order to determine the driving situation, unavoidable sensor measurement errors can have a negative impact on both the safety and the satisfaction of the customer. In this paper, it is shown how a new methodology for the robust design of sensors and functions in vehicular safety considering sensor measurement errors that has already been applied to design an automatic emergency braking (AEB) system can also be applied to design an automatic emergency steering (AES) system. Based on a stochastic model, we formulate the robust design as optimization problems, whose solution yields the optimal parameters for the AES system with respect to a probabilistic quality measure. The probabilistic quality measure is defined similarly to that for the robust design of the AEB system and a closed-form expression is derived for it in case of circular vehicle shapes and an emergency steer intervention with constant lateral acceleration. For more complex vehicle shapes and emergency steer interventions, an approximation of the probabilistic quality measure by a Monte Carlo simulation is proposed in this paper, which leads to a robust design which is based on simulations of the vehicular safety system under design and applicable to other vehicular safety systems as well.