Virtual sensor models for real-time applications
Open Access
- 28 September 2016
- journal article
- Published by Copernicus GmbH in Advances in Radio Science
- Vol. 14, 31-37
- https://doi.org/10.5194/ars-14-31-2016
Abstract
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.Keywords
This publication has 21 references indexed in Scilit:
- Learning Traffic Light Parameters with Floating Car DataPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Backend Systems for ADASPublished by Springer Science and Business Media LLC ,2015
- Virtual Stochastic Testing of Advanced Driver Assistance SystemsPublished by Springer Science and Business Media LLC ,2015
- A non-parametric approach for modeling sensor behaviorPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Radar target generationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Generic architecture for simulation of ADAS sensorsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Autonomes FahrenPublished by Springer Science and Business Media LLC ,2015
- Crowdsourced intersection parameters: A generic approach for extraction and confidence estimationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- Modeling and validation of a new generic virtual optical sensor for ADAS prototypingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2012
- Large Sample Techniques for StatisticsPublished by Springer Science and Business Media LLC ,2010