Bounding GPS-Based Positioning and Navigation Uncertainty for Autonomous Drifting via Reachability

Asta Wu, Adyasha Mohanty, Anonto Zaman, Grace Gao

Abstract: The study of autonomous drifting is motivated by the need to ensure the safe operation of autonomous vehicles, particularly under extreme dynamics where driver safety may be compromised. In such safety-critical applications, the worst-case positioning performance under measurement uncertainty must be considered. Reachability analysis (RA) is a technique that is commonly used to provide formal guarantees on the position under such uncertainty. In this work, we integrate a set-based RA estimation framework with nonlinear drift dynamics and double-difference GPS pseudorange measurements to provide positioning bounds for an autonomous drifting vehicle. We validate our framework on high-fidelity simulated drifting experiments with varied GPS measurement noise profiles. We show that the positioning bounds from our framework successfully bound the trajectories from a particle filter across all validation cases.
Published in: Proceedings of the 36th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2023)
September 11 - 15, 2023
Hyatt Regency Denver
Denver, Colorado
Pages: 712 - 726
Cite this article: Wu, Asta, Mohanty, Adyasha, Zaman, Anonto, Gao, Grace, "Bounding GPS-Based Positioning and Navigation Uncertainty for Autonomous Drifting via Reachability," Proceedings of the 36th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2023), Denver, Colorado, September 2023, pp. 712-726. https://doi.org/10.33012/2023.19229
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