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  4. Automotive LiDAR Based Precipitation State Estimation Using Physics Informed Spatio-Temporal 3D Convolutional Neural Networks (PIST-CNN)
 
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2023
Conference Paper
Title

Automotive LiDAR Based Precipitation State Estimation Using Physics Informed Spatio-Temporal 3D Convolutional Neural Networks (PIST-CNN)

Abstract
With the rise of driving automation, optical sensors like cameras and LiDAR are playing a crucial role in vehicle perception. However, these sensors face challenges from harsh environmental conditions, including extreme temperatures and weather effects like fog and precipitation, which degrade their
performance due to particle scattering. Consequently, significant efforts are being made to understand, model, and mitigate these effects. In this work, we address the reverse research question and demonstrate that the precise present precipitation state in form of the particle size and velocity distribution can be
predicted using degraded Automotive LiDAR measurements and spatio-temporal 3D convolutional neural networks. By that, this approach provides 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 weighted heterogeneous sensor data fusion and adaptive filtering during data pre-processing.
Author(s)
Kettelgerdes, Marcel
Fraunhofer-Institut für Verkehrs- und Infrastruktursysteme IVI  
Pandey, Amit
Technische Hochschule Ingolstadt
Unruh, Denis
Technische Hochschule Ingolstadt
Erdogan, Hüseyin
Cont Temic microelectronic GmbH
Wunderle, Bernhard
Technische Universität Chemnitz  
Elger, Gordon  
Fraunhofer-Institut für Verkehrs- und Infrastruktursysteme IVI  
Mainwork
29th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2023  
Project(s)
Funktions- und Verkehrssicherheit für Automatisierte und Vernetzte Mobilität – Nutzen für die Gesellschaft und oekologische Wirkung
Funder
Bundesministerium für Digitales und Verkehr  
Conference
International Conference on Mechatronics and Machine Vision in Practice 2023  
DOI
10.1109/M2VIP58386.2023.10413394
Language
English
Fraunhofer-Institut für Verkehrs- und Infrastruktursysteme IVI  
Keyword(s)
  • ADAS

  • adverse weather conditions

  • artificial intelligence

  • automotive

  • LiDAR

  • particle size distribution

  • precipitation prediction

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