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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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Principais autores: Kelvin Walenta, Simon Genser, Selim Solmaz
Format: Artigo
Sprog:Inglês
Udgivet: MDPI AG 2024-03-01
Serier:Sensors
Fag:
virtual validation
sensor simulation
ADAS/AD development
Gaussian mixture models
radar modeling
data-driven modeling
Online adgang:https://www.mdpi.com/1424-8220/24/7/2177
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