A Methodology to Model the Rain and Fog Effect on the Performance of Automotive LiDAR Sensors
Arsalan Haider,
Marcell Pigniczki,
Shotaro Koyama,
Michael H. Köhler,
Lukas Haas,
Maximilian Fink,
Michael Schardt,
Koji Nagase,
Thomas Zeh,
Abdulkadir Eryildirim,
Tim Poguntke,
Hideo Inoue,
Martin Jakobi,
Alexander W. Koch
Affiliations
Arsalan Haider
Institute for Driver Assistance Systems and Connected Mobility (IFM), Kempten University of Applied Sciences, Junkersstrasse 1A, 87734 Benningen, Germany
Marcell Pigniczki
Institute for Driver Assistance Systems and Connected Mobility (IFM), Kempten University of Applied Sciences, Junkersstrasse 1A, 87734 Benningen, Germany
Shotaro Koyama
Advanced Vehicle Research Institute, Kanagawa Institute of Technology, Shimoogino 1030, Atsugi 243-0292, Japan
Institute for Driver Assistance Systems and Connected Mobility (IFM), Kempten University of Applied Sciences, Junkersstrasse 1A, 87734 Benningen, Germany
Maximilian Fink
Institute for Measurement Systems and Sensor Technology, Technical University of Munich, Theresienstrasse 90, 80333 Munich, Germany
Advanced Vehicle Research Institute, Kanagawa Institute of Technology, Shimoogino 1030, Atsugi 243-0292, Japan
Thomas Zeh
Institute for Driver Assistance Systems and Connected Mobility (IFM), Kempten University of Applied Sciences, Junkersstrasse 1A, 87734 Benningen, Germany
Abdulkadir Eryildirim
Infineon Technologies Austria AG, 4040 Linz, Austria
Tim Poguntke
Institute for Driver Assistance Systems and Connected Mobility (IFM), Kempten University of Applied Sciences, Junkersstrasse 1A, 87734 Benningen, Germany
Hideo Inoue
Advanced Vehicle Research Institute, Kanagawa Institute of Technology, Shimoogino 1030, Atsugi 243-0292, Japan
Martin Jakobi
Institute for Measurement Systems and Sensor Technology, Technical University of Munich, Theresienstrasse 90, 80333 Munich, Germany
Alexander W. Koch
Institute for Measurement Systems and Sensor Technology, Technical University of Munich, Theresienstrasse 90, 80333 Munich, Germany
In this work, we introduce a novel approach to model the rain and fog effect on the light detection and ranging (LiDAR) sensor performance for the simulation-based testing of LiDAR systems. The proposed methodology allows for the simulation of the rain and fog effect using the rigorous applications of the Mie scattering theory on the time domain for transient and point cloud levels for spatial analyses. The time domain analysis permits us to benchmark the virtual LiDAR signal attenuation and signal-to-noise ratio (SNR) caused by rain and fog droplets. In addition, the detection rate (DR), false detection rate (FDR), and distance error derror of the virtual LiDAR sensor due to rain and fog droplets are evaluated on the point cloud level. The mean absolute percentage error (MAPE) is used to quantify the simulation and real measurement results on the time domain and point cloud levels for the rain and fog droplets. The results of the simulation and real measurements match well on the time domain and point cloud levels if the simulated and real rain distributions are the same. The real and virtual LiDAR sensor performance degrades more under the influence of fog droplets than in rain.
