Evaluating INS/GNSS/LiDAR Availability for Self-Driving Cars in Urban Environments

Kana Nagai, Matthew Spenko, Ron Henderson, Boris Pervan

Abstract: We evaluate INS/GNSS-based navigation availability against prospective integrity requirements for self-driving cars in urban environments. Specifically, we quantify how long and under what local conditions a practical integrated navigation system, consisting of GNSS, INS, wheel speed sensors, 3-D maps, and vehicle dynamic constraints, can maintain fault-free integrity along a real urban street transect in downtown Chicago. The results show that the integrated system cannot ensure availability across the entire transect, even with four GNSS constellations, and that the main factors influencing unavailable regions are building height, IMU grade, alert limit, and vehicle speed. After the deficient locations are exposed, we consider landmark localization by LiDAR to maintain continuous navigation. We derive ranging error models and investigate placement configurations of pole-like landmarks to ensure availability of INS/GNSS/LiDAR self-driving car fault-free integrity.
Published in: Proceedings of the 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021)
September 20 - 24, 2021
Union Station Hotel
St. Louis, Missouri
Pages: 2121 - 2132
Cite this article: Nagai, Kana, Spenko, Matthew, Henderson, Ron, Pervan, Boris, "Evaluating INS/GNSS/LiDAR Availability for Self-Driving Cars in Urban Environments," Proceedings of the 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021), St. Louis, Missouri, September 2021, pp. 2121-2132. https://doi.org/10.33012/2021.18058
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