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Research Article
Sep 24, 2021

Effective Range Assessment of Lidar Imaging Systems for Autonomous Vehicles Under Adverse Weather Conditions With Stationary Vehicles

Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
Volume 8, Issue 3

Abstract

Light detection and ranging (lidar) imaging systems are being increasingly used in autonomous vehicles. However, the final technology implementation is still undetermined as major automotive manufacturers are only starting to select providers for data collection units that can be introduced in commercial vehicles. Currently, testing for autonomous vehicles is mostly performed in sunny environments. Experiments conducted in good weather cannot provide information regarding performance quality under extreme conditions such as fog, rain, and snow. Under extreme conditions, many instances of false detection may arise because of the backscattered intensity, thereby reducing the reliability of the sensor. In this work, lidar sensors were tested in adverse weather to understand how extreme weather affects data collection. Testing setup and algorithms were developed for this purpose. The results are expected to provide technological validation for the commercial use of lidar in automated vehicles. The effective ranges of two popular lidar sensors were estimated under adverse weather conditions, namely, fog, rain, and snow. Results showed that fog severely affected lidar performance, and rain too had some effect on the performance. Meanwhile, snow did not affect lidar performance. This article is available in the ASME Digital Collection at https://doi.org/10.1115/1.4052228.

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Information

Published In

Go to ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
Volume 8Issue 3September 2022

History

Received: Apr 17, 2021
Revision received: Aug 14, 2021
Published online: Sep 24, 2021
Published in print: Sep 1, 2022

Authors

Affiliations

Mem. ASME
Department of Physics and Engineering, Frostburg State University, 101 Braddock Road, CSC 105, Frostburg, MD 21532 e-mail: [email protected]
Spencer Hamblin [email protected]
Physics and Engineering Department, Frostburg State University, 101 Braddock Road, Frostburg, MD 21532 e-mail: [email protected]
Genshe Chen [email protected]
Intelligent Fusion Technology, Inc., 20271 Goldenrod Lane, Suite 2066, Germantown, MD 20876 e-mail: [email protected]

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