Communication Dans Un Congrès Année : 2025

High Integrity Localization with Bayesian Optimization for Information Filter Tuning with Fault Detection

Résumé

Safe navigation of autonomous vehicles relies on high integrity localization system based on a state estimation method, which strongly depends on the choice of parameters, such as the measurement noise covariance matrix. To avoid filter divergence, a fault detection and exclusion (FDE) procedure is also required. Selecting appropriate thresholds for the FDE step is challenging and affects the accuracy and the integrity of localization. This paper presents a multi-sensor data fusion based on the Information Filter (IF) with an auto-tuning method that relies on Bayesian Optimization (BO). The proposed method aims to optimize the measurement covariance matrix and the FDE thresholds. BO is well suited for non-convex and stochastic cost functions with local minima. A novel objective function is also designed to improve the accuracy and guarantee the integrity of the estimates, by ensuring consistent uncertainty regions. The objective function consists of two terms: one related to the error and the other to the quantile. The proposed approach is evaluated with experimental data from a vehicle equipped with wheel speed sensors, fused with Global Navigation Satellite System (GNSS) pseudorange measurements.

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Dates et versions

hal-05164814 , version 1 (01-09-2025)

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Mohammed Salhi, Joelle Al Hage. High Integrity Localization with Bayesian Optimization for Information Filter Tuning with Fault Detection. 28th International Conference on Information Fusion (FUSION 2025), Jul 2025, Rio de Janeiro, Brazil. pp.1-8, ⟨10.23919/FUSION65864.2025.11124118⟩. ⟨hal-05164814⟩
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