Publication

Quantifying automotive lidar system uncertainty in adverse weather: mathematical models and validation

Alavi, B.
; 
Illing, Thomas
; 
Campean, Felician
; 
Spencer, Paul
; 
Abdullatif, Amr R.A.
Publication Date
2025-07-23
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Keywords
Lidar, Adverse weather, Rain, Fog, Snow, Dust, Particulate matter, Simulation, Carla, Robot Operating System 2
Rights
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Peer-Reviewed
Yes
Open Access status
openAccess
Accepted for publication
2025-07-17
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Engineering and Digital Technology Publications
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Spencer_et_al_2025.pdf
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Abstract
Lidar technology is a key sensor for autonomous driving due to its precise environmental perception. However, adverse weather and atmospheric conditions involving fog, rain, snow, dust, and smog can impair lidar performance, leading to potential safety risks. This paper introduces a comprehensive methodology to simulate lidar systems under such conditions and validate the results against real-world experiments. Existing empirical models for the extinction and backscattering of laser beams are analyzed, and new models are proposed for dust storms and smog, derived using Mie theory. These models are implemented in the CARLA simulator and evaluated using Robot Operating System 2 (ROS 2). The simulation methodology introduced allowed the authors to set up test experiments replicating real-world conditions, to validate the models against real-world data available in the literature, and to predict the performance of the lidar system in all weather conditions. This approach enables the development of virtual test scenarios for corner cases representing rare weather conditions to improve robustness and safety in autonomous systems.
URI
https://bradscholars.brad.ac.uk/handle/10454/20553
Version
Published version
Citation
Alavi B, Illing T, Campean F et al (2025) Quantifying automotive lidar system uncertainty in adverse weather: mathematical models and validation. Applied Sciences. 15(15): 8191.
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Link to published version
Link to Version of Record
https://doi.org/10.3390/app15158191
Type
Article
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