Precise Adverse Weather Characterization by Deep-Learning-Based Noise Processing in Automotive LiDAR Sensors

Precise Adverse Weather Characterization by Deep-Learning-Based Noise Processing in Automotive LiDAR Sensors

With current advances in automated driving, optical sensors like cameras and LiDARs are playing an increasingly important role in modern driver assistance systems. However, these sensors face challenges from adverse weather effects like fog and precipitation, which significantly degrade the sensor p...

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Autors principals:	Marcel Kettelgerdes, Nicolas Sarmiento, Hüseyin Erdogan, Bernhard Wunderle, Gordon Elger
Format:	Article
Idioma:	English
Publicat:	MDPI AG 2024-06-01
Col·lecció:	Remote Sensing
Matèries:	ADAS adverse weather weather classification artificial intelligence deep learning Vision Transformer
Accés en línia:	https://www.mdpi.com/2072-4292/16/13/2407

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