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...

Szczegółowa specyfikacja

Opis bibliograficzny
Główni autorzy: Marcel Kettelgerdes, Nicolas Sarmiento, Hüseyin Erdogan, Bernhard Wunderle, Gordon Elger
Format: Artykuł
Język:English
Wydane: MDPI AG 2024-06-01
Seria:Remote Sensing
Hasła przedmiotowe:
ADAS
adverse weather
weather classification
artificial intelligence
deep learning
Vision Transformer
Dostęp online:https://www.mdpi.com/2072-4292/16/13/2407

Internet

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

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