# HEAD: A Bandwidth-Efficient Cooperative Perception Approach for Heterogeneous Connected and Autonomous Vehicles

arXiv: https://arxiv.org/abs/2408.15428 · HTML: https://mlsanity.com/paper/2408.15428/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 30 references that are in the map.
- First posted on arXiv 27 Aug 2024.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](https://mlsanity.com/paper/1812.04244/) (2018)
- [V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer](https://mlsanity.com/paper/2203.10638/) (2022)
- [OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication](https://mlsanity.com/paper/2109.07644/) (2021)
- [V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction](https://mlsanity.com/paper/2008.07519/) (2020)
- [DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection](https://mlsanity.com/paper/2204.05575/) (2022)
- [Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps](https://mlsanity.com/paper/2209.12836/) (2022)
- [CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers](https://mlsanity.com/paper/2207.02202/) (2022)
- [F-Cooper: Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Clouds](https://mlsanity.com/paper/1909.06459/) (2019)
- [Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Clouds](https://mlsanity.com/paper/1905.05265/) (2019)
- [V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception](https://mlsanity.com/paper/2303.07601/) (2023)
- [Activation Functions: Comparison of trends in Practice and Research for Deep Learning](https://mlsanity.com/paper/1811.03378/) (2018)
- [Robust Collaborative 3D Object Detection in Presence of Pose Errors](https://mlsanity.com/paper/2211.07214/) (2022)
- [Cooperative Perception for 3D Object Detection in Driving Scenarios using Infrastructure Sensors](https://mlsanity.com/paper/1912.12147/) (2019)
- [CoFF: Cooperative Spatial Feature Fusion for 3D Object Detection on Autonomous Vehicles](https://mlsanity.com/paper/2009.11975/) (2020)
- [MACP: Efficient Model Adaptation for Cooperative Perception](https://mlsanity.com/paper/2310.16870/) (2023)
- [SiCP: Simultaneous Individual and Cooperative Perception for 3D Object Detection in Connected and Automated Vehicles](https://mlsanity.com/paper/2312.04822/) (2023)

## Built on by (most cited first, 4 of 4)

- [M3CAD: Towards Generic Cooperative Autonomous Driving Benchmark](https://mlsanity.com/paper/2505.06746/) (2025)
- [HeCoFuse: Cross-Modal Complementary V2X Cooperative Perception with Heterogeneous Sensors](https://mlsanity.com/paper/2507.13677/) (2025)
- [From Features to Reference Points: Lightweight and Adaptive Fusion for Cooperative Autonomous Driving](https://mlsanity.com/paper/2511.18757/) (2025)
- [CooperDrive: Enhancing Driving Decisions Through Cooperative Perception](https://mlsanity.com/paper/2604.14454/) (2026)

## Abstract (arXiv)

In cooperative perception studies, there is often a trade-off between communication bandwidth and perception performance. While current feature fusion solutions are known for their excellent object detection performance, transmitting the entire sets of intermediate feature maps requires substantial bandwidth. Furthermore, these fusion approaches are typically limited to vehicles that use identical detection models. Our goal is to develop a solution that supports cooperative perception across vehicles equipped with different modalities of sensors. This method aims to deliver improved perception performance compared to late fusion techniques, while achieving precision similar to the state-of-art intermediate fusion, but requires an order of magnitude less bandwidth. We propose HEAD, a method that fuses features from the classification and regression heads in 3D object detection networks. Our method is compatible with heterogeneous detection networks such as LiDAR PointPillars, SECOND, VoxelNet, and camera Bird's-eye View (BEV) Encoder. Given the naturally smaller feature size in the detection heads, we design a self-attention mechanism to fuse the classification head and a complementary feature fusion layer to fuse the regression head. Our experiments, comprehensively evaluated on the V2V4Real and OPV2V datasets, demonstrate that HEAD is a fusion method that effectively balances communication bandwidth and perception performance.