# Multi-Modal Sensor Fusion for Proactive Blockage Prediction in mmWave Vehicular Networks

arXiv: https://arxiv.org/abs/2507.15769 · HTML: https://mlsanity.com/paper/2507.15769/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 14 references that are in the map.
- First posted on arXiv 21 Jul 2025.

## Builds on (most cited first, 8 of 8)

- [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019)
- [Deep Learning in Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/1803.04311/) (2018)
- [Towards an Intelligent Edge: Wireless Communication Meets Machine Learning](https://mlsanity.com/paper/1809.00343/) (2018)
- [DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset](https://mlsanity.com/paper/2211.09769/) (2022)
- [Multi-Modal Beam Prediction Challenge 2022: Towards Generalization](https://mlsanity.com/paper/2209.07519/) (2022)
- [Towards a Wireless Physical-Layer Foundation Model: Challenges and Strategies](https://mlsanity.com/paper/2403.12065/) (2024)
- [Radar Aided Proactive Blockage Prediction in Real-World Millimeter Wave Systems](https://mlsanity.com/paper/2111.14805/) (2021)
- [Vehicular Blockage Modelling and Performance Analysis for mmWave V2V Communications](https://mlsanity.com/paper/2110.10576/) (2021)

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

- [Enwar 3.0: An Agentic Multi-Modal LLM Orchestrator for Situation-Aware Beamforming, Blockage Prediction, and Handover Management](https://mlsanity.com/paper/2605.03215/) (2026)

## Abstract (arXiv)

Vehicular communication systems operating in the millimeter wave (mmWave) band are highly susceptible to signal blockage from dynamic obstacles such as vehicles, pedestrians, and infrastructure. To address this challenge, we propose a proactive blockage prediction framework that utilizes multi-modal sensing, including camera, GPS, LiDAR, and radar inputs in an infrastructure-to-vehicle (I2V) setting. This approach uses modality-specific deep learning models to process each sensor stream independently and fuses their outputs using a softmax-weighted ensemble strategy based on validation performance. Our evaluations, for up to 1.5s in advance, show that the camera-only model achieves the best standalone trade-off with an F1-score of 97.1% and an inference time of 89.8ms. A camera+radar configuration further improves accuracy to 97.2% F1 at 95.7ms. Our results display the effectiveness and efficiency of multi-modal sensing for mmWave blockage prediction and provide a pathway for proactive wireless communication in dynamic environments.