Bayesian Gaussian Mixture Models for Enhanced Radar Sensor Modeling: A Data-Driven Approach towards Sensor Simulation for ADAS/AD Development

In the realm of road safety and the evolution toward automated driving, Advanced Driver Assistance and Automated Driving (ADAS/AD) systems play a pivotal role. As the complexity of these systems grows, comprehensive testing becomes imperative, with virtual test environments becoming crucial, especia...

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Autors principals:	Kelvin Walenta, Simon Genser, Selim Solmaz
Format:	Artigo
Idioma:	Inglês
Publicat:	MDPI AG 2024-03-01
Col·lecció:	Sensors
Matèries:	virtual validation sensor simulation ADAS/AD development Gaussian mixture models radar modeling data-driven modeling
Accés en línia:	https://www.mdpi.com/1424-8220/24/7/2177
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