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  4. Precise Adverse Weather Characterization by Deep-Learning-Based Noise Processing in Automotive LiDAR Sensors
 
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2024
Journal Article
Title

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

Abstract
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 performance due to scattering effects in its optical path. Consequently, major efforts are being made to understand, model, and mitigate these effects. In this work, the reverse research question is investigated, demonstrating that these measurement effects can be exploited to predict occurring weather conditions by using state-of-the-art deep learning mechanisms. In order to do so, a variety of models have been developed and trained on a recorded multiseason dataset and benchmarked with respect to performance, model size, and required computational resources, showing that especially modern vision transformers achieve remarkable results in distinguishing up to 15 precipitation classes with an accuracy of 84.41% and predicting the corresponding precipitation rate with a mean absolute error of less than 0.47 mm/h, solely based on measurement noise. Therefore, this research may contribute to a cost-effective solution for characterizing precipitation with a commercial Flash LiDAR sensor, which can be implemented as a lightweight vehicle software feature to issue advanced driver warnings, adapt driving dynamics, or serve as a data quality measure for adaptive data preprocessing and fusion.
Author(s)
Kettelgerdes, Marcel
Fraunhofer-Institut für Verkehrs- und Infrastruktursysteme IVI  
Sarmiento, Nicolas
Fraunhofer-Institut für Verkehrs- und Infrastruktursysteme IVI  
Erdogan, Hüseyin
Conti Temic Microelectronic GmbH
Wunderle, Bernhard
TU Chemnitz  
Elger, Gordon  
Fraunhofer-Institut für Verkehrs- und Infrastruktursysteme IVI  
Journal
Remote sensing  
Project(s)
Funktions- und Verkehrs-Sicherheit für Automatisierte und Vernetzte Mobilität - Nutzen für die Gesellschaft und oekologische Wirkung; Teilvorhaben: Entwicklung eines Raytracing-basierten, physikalischen Lidar-Sensormodells und Validierung  
Production, After-Sales und PLC - Across Automated Driving; Teilvorhaben: Informationsmodellierung für die Domänen Produktion und Supply Chain, Digitale Zwillinge  
Funder
Bundesministerium für Digitales und Verkehr  
Bundesministerium für Wirtschaft und Klimaschutz -BMWK-
Open Access
DOI
10.3390/rs16132407
Language
English
Fraunhofer-Institut für Verkehrs- und Infrastruktursysteme IVI  
Keyword(s)
  • ADAS

  • adverse weather

  • weather classification

  • artifial intelligence

  • deep learning

  • Vision Transformer

  • LSTM

  • automotive

  • LiDAR

  • precipitation measurement

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