Integrity with Extraction Faults in LiDAR-Based Urban Navigation for Driverless Vehicles

Kana Nagai, Yihe Chen, Matthew Spenko, Ron Henderson, Boris Pervan

Abstract:	Abstract—This paper examines the safety of LiDAR-based navigation for driverless vehicles and aims to reduce the risk of extracting information from undesired obstacles. We define the faults of a LiDAR navigation system, derive the integrity risk equation, and suggest landmark environments to reduce the risk of fault-free position error and data association faults. We also present a method to quantify feature extraction risk using reflective tape on desired landmarks to enhance the intensity of returned signals. The high-intensity returns are used in feature extraction decisions between obstacles and pre-defined landmarks using the Neyman-Pearson Lemma. Our experiments demonstrate that the probability of incorrect extraction is below 10?14, and the method is sufficient to ensure safety. Index Terms—integrity, LiDAR, urban navigation, driverless vehicle
Published in:	2023 IEEE/ION Position, Location and Navigation Symposium (PLANS) April 24 - 27, 2023 Hyatt Regency Hotel Monterey, CA
Pages:	1099 - 1106
Cite this article:	Nagai, Kana, Chen, Yihe, Spenko, Matthew, Henderson, Ron, Pervan, Boris, "Integrity with Extraction Faults in LiDAR-Based Urban Navigation for Driverless Vehicles," 2023 IEEE/ION Position, Location and Navigation Symposium (PLANS), Monterey, CA, April 2023, pp. 1099-1106. https://doi.org/10.1109/PLANS53410.2023.10140132
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