Model-Based Analysis of Sensor-Noise in Predictive Passive Safety Algorithms
Tobias Dirndorfer, Michael Botsch, Alois Knoll · 2011
Paper Number 11-0251 The introduction of environment perception sensors into the automotive world enables further improve-ment of the already highly optimized passive safety systems. Such sensors facilitate the development of safety applications that can act in a context sen-sitive manner concerning the protection of vehicle occupants. Hereby the quality of the provided in-formation is decisive for the usability and effective range of such sensors within integrated safety sys-tems. In this paper noise effects in sensors and their implications on the prediction of collision parame-ters are analyzed. The focus lies on sensors that can measure distances but not velocities or acceler-ations of the objects surrounding the car. For such sensors a noise model is presented as well as a track-ing algorithm aiming to estimate the velocities and to compensate the effects of noise. This information is used by a trajectory-based algorithm to predict relevant collision parameters like time-to-collision, relative velocity at collision time etc. Monte Carlo simulations show the influence of noise on the ac-curacy of the predicted collision parameters. The described model-based study allows the systematic deduction of sensor requirements and represents a new way for the evaluation of the robustness of pre-dictive passive safety systems.