The navigation of intelligent vehicles relies on high integrity localization system capable to bound the estimation errors. This paper introduces a zonotopic and Gaussian Kalman filter in informational form for multi-sensor data fusion and confidence domain computation. By integrating stochastic and set membership uncertainties, the proposed filter ensures accurate localization with a non pessimistic confidence domain, thus addressing the challenges posed by traditional techniques. Taking advantage of the informational form, a fault detection and exclusion step is added to enhance filter robustness. Following a zonotope reduction step, a confidence domain computation, considering both Gaussian and zonotopic uncertainties, is proposed in the context of intelligent vehicles. The accuracy and integrity of the approach are assessed using experimental data, including the fusion of GPS and Galileo pseudoranges with camera measurements after a map matching step. Additionally, a comparative analysis is conducted with the classical Kalman filter.