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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Bibliografiska uppgifter
Huvudupphovsmän: Marcel Kettelgerdes, Nicolas Sarmiento, Hüseyin Erdogan, Bernhard Wunderle, Gordon Elger
Materialtyp: Artikel
Språk:English
Publicerad: MDPI AG 2024-06-01
Serie:Remote Sensing
Ämnen:
ADAS
adverse weather
weather classification
artificial intelligence
deep learning
Vision Transformer
Länkar:https://www.mdpi.com/2072-4292/16/13/2407

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https://www.mdpi.com/2072-4292/16/13/2407

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