Multimodal Radio and Vision Fusion for Robust Localization in Urban V2I Communications

Paper in proceeding, 2026

Accurate localization is critical for vehicle-toinfrastructure (V2I) communication systems, especially in urban areas where GPS signals are often obstructed by tall buildings, leading to significant positioning errors, necessitating alternative or complementary techniques for reliable and precise positioning in applications like autonomous driving and smart city infrastructure. This paper proposes a multimodal contrastive learningbased regression framework for V2I localization. By integrating channel state information (CSI) with visual data, the framework achieves enhanced accuracy and reliability. The approach leverages the complementary strengths of wireless and visual data to overcome the limitations of traditional localization methods, offering a robust solution for V2I applications. Simulation results demonstrate that the proposed CSI-vision fusion model significantly surpasses both traditional and unimodal benchmarks, delivering superior localization precision in challenging urban environments.