# Vehicle Cameras Guide mmWave Beams: Approach and Real-World V2V Demonstration

arXiv: https://arxiv.org/abs/2308.10362 · HTML: https://mlsanity.com/paper/2308.10362/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 20 Aug 2023.

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

- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset](https://mlsanity.com/paper/2211.09769/) (2022)
- [Real-Time Digital Twins: Vision and Research Directions for 6G and Beyond](https://mlsanity.com/paper/2301.11283/) (2023)
- [Vision-Position Multi-Modal Beam Prediction Using Real Millimeter Wave Datasets](https://mlsanity.com/paper/2111.07574/) (2021)
- [LiDAR Aided Future Beam Prediction in Real-World Millimeter Wave V2I Communications](https://mlsanity.com/paper/2203.05548/) (2022)
- [Radar Aided 6G Beam Prediction: Deep Learning Algorithms and Real-World Demonstration](https://mlsanity.com/paper/2111.09676/) (2021)
- [Digital Twin Based Beam Prediction: Can we Train in the Digital World and Deploy in Reality?](https://mlsanity.com/paper/2301.07682/) (2023)
- [Reinforcement Learning of Beam Codebooks in Millimeter Wave and Terahertz MIMO Systems](https://mlsanity.com/paper/2102.11392/) (2021)

## Built on by (most cited first, 3 of 3)

- [Multi-Modality Sensing in mmWave Beamforming for Connected Vehicles Using Deep Learning](https://mlsanity.com/paper/2504.06173/) (2025)
- [When Simultaneous Localization and Mapping Meets Wireless Communications: A Survey](https://mlsanity.com/paper/2602.06995/) (2026)
- [A Camera-Cooperative ISAC Framework for Multimodal Non-Cooperative UAVs Sensing](https://mlsanity.com/paper/2605.22090/) (2026)

## Abstract (arXiv)

Accurately aligning millimeter-wave (mmWave) and terahertz (THz) narrow beams is essential to satisfy reliability and high data rates of 5G and beyond wireless communication systems. However, achieving this objective is difficult, especially in vehicle-to-vehicle (V2V) communication scenarios, where both transmitter and receiver are constantly mobile. Recently, additional sensing modalities, such as visual sensors, have attracted significant interest due to their capability to provide accurate information about the wireless environment. To that end, in this paper, we develop a deep learning solution for V2V scenarios to predict future beams using images from a 360 camera attached to the vehicle. The developed solution is evaluated on a real-world multi-modal mmWave V2V communication dataset comprising co-existing 360 camera and mmWave beam training data. The proposed vision-aided solution achieves ≈ 85% top-5 beam prediction accuracy while significantly reducing the beam training overhead. This highlights the potential of utilizing vision for enabling highly-mobile V2V communications.