TY - JOUR
T1 - Virtual sensor models for real-time applications
AU - Hirsenkorn, Nils
AU - Hanke, Timo
AU - Rauch, Andreas
AU - Dehlink, Bernhard
AU - Rasshofer, Ralph
AU - Biebl, Erwin
N1 - Publisher Copyright:
© 2016 Author(s).
PY - 2016/9/28
Y1 - 2016/9/28
N2 - Increased complexity and severity of future driver assistance systems demand extensive testing and validation. As supplement to road tests, driving simulations offer various benefits. For driver assistance functions the perception of the sensors is crucial. Therefore, sensors also have to be modeled. In this contribution, a statistical data-driven sensor-model, is described. The state-space based method is capable of modeling various types behavior. In this contribution, the modeling of the position estimation of an automotive radar system, including autocorrelations, is presented. For rendering real-time capability, an efficient implementation is presented.
AB - Increased complexity and severity of future driver assistance systems demand extensive testing and validation. As supplement to road tests, driving simulations offer various benefits. For driver assistance functions the perception of the sensors is crucial. Therefore, sensors also have to be modeled. In this contribution, a statistical data-driven sensor-model, is described. The state-space based method is capable of modeling various types behavior. In this contribution, the modeling of the position estimation of an automotive radar system, including autocorrelations, is presented. For rendering real-time capability, an efficient implementation is presented.
UR - http://www.scopus.com/inward/record.url?scp=84990033132&partnerID=8YFLogxK
U2 - 10.5194/ars-14-31-2016
DO - 10.5194/ars-14-31-2016
M3 - Article
AN - SCOPUS:84990033132
SN - 1684-9965
VL - 14
SP - 31
EP - 37
JO - Advances in Radio Science
JF - Advances in Radio Science
ER -