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

arXiv: https://arxiv.org/abs/2508.17640 · HTML: https://mlsanity.com/paper/2508.17640/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2025.

## Built on by (most cited first, 1 of 1)

- [Vision-Wireless Fusion for Multi-User Localization: A Cross-Modal Transformer Approach](https://mlsanity.com/paper/2609.23372/) (2026)

## Abstract (arXiv)

Accurate localization is critical for vehicle-to-infrastructure (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 learning regression based localization framework for V2I scenarios that combines channel state information (CSI) with visual information to achieve improved 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 and vision fusion model significantly outperforms traditional methods and single modal models, achieving superior localization accuracy and precision in complex urban environments.