Fault-Free Integrity and Continuity for Driverless Urban Vehicle Navigation with Multi-Sensor Integration: A Case Study in Downtown Chicago

Kana Nagai, Matthew Spenko, Ron Henderson, Boris Pervan

Abstract:	Driverless vehicles must operate with a safety integrity level, but urban environments degrade GNSS navigation accuracy and thereby fault-free integrity. Integration with INS helps maintain continuity, but position errors drift over time without GNSS signals. Whether modern navigation systems can provide satisfactory integrity for driverless vehicles throughout a city is still undetermined. This research investigates how GNSS and INS, when appropriately augmented by LiDAR ranging from local landmarks, can safely navigate vehicles through a real-world urban environment. We begin by defining safety requirements for driverless vehicles under fault-free conditions and developing measurement models for multi-sensor integrated navigation systems using an EKF. The critical elements of urban navigation are then discussed, including individual INS noise parameter specifications and the effect of velocity updates. Simulations along a nine-kilometer-long urban transect in downtown Chicago show that velocity updates can extend navigation continuity by bridging intermittent GNSS signal availability. However, position reference updates at 35-meter intervals, in our case LiDAR ranges from local landmarks, are needed to achieve 100% navigation availability through the transect.
Published in:	Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022) September 19 - 23, 2022 Hyatt Regency Denver Denver, Colorado
Pages:	1350 - 1365
Cite this article:	Nagai, Kana, Spenko, Matthew, Henderson, Ron, Pervan, Boris, "Fault-Free Integrity and Continuity for Driverless Urban Vehicle Navigation with Multi-Sensor Integration: A Case Study in Downtown Chicago," Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022), Denver, Colorado, September 2022, pp. 1350-1365. https://doi.org/10.33012/2022.18319
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