# On the Role of Sensor Fusion for Object Detection in Future Vehicular Networks

arXiv: https://arxiv.org/abs/2104.11785 · HTML: https://mlsanity.com/paper/2104.11785/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 28 references that are in the map.
- First posted on arXiv 23 Apr 2021.

## Builds on (most cited first, 16 of 16)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [Multi-View 3D Object Detection Network for Autonomous Driving](https://mlsanity.com/paper/1611.07759/) (2016)
- [Deep Learning for 3D Point Clouds: A Survey](https://mlsanity.com/paper/1912.12033/) (2019)
- [PointPainting: Sequential Fusion for 3D Object Detection](https://mlsanity.com/paper/1911.10150/) (2019)
- [Joint 3D Proposal Generation and Object Detection from View Aggregation](https://mlsanity.com/paper/1712.02294/) (2017)
- [PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation](https://mlsanity.com/paper/1711.10871/) (2017)
- [Imbalance Problems in Object Detection: A Review](https://mlsanity.com/paper/1909.00169/) (2019)
- [Cooperative Perception for 3D Object Detection in Driving Scenarios using Infrastructure Sensors](https://mlsanity.com/paper/1912.12147/) (2019)
- [Hybrid Point Cloud Semantic Compression for Automotive Sensors: A Performance Evaluation](https://mlsanity.com/paper/2103.03819/) (2021)
- [Multi-Sensor 3D Object Box Refinement for Autonomous Driving](https://mlsanity.com/paper/1909.04942/) (2019)
- [Investigating Value of Information in Future Vehicular Communications](https://mlsanity.com/paper/1907.10124/) (2019)
- [On the Feasibility of Integrating mmWave and IEEE 802.11p for V2V Communications](https://mlsanity.com/paper/1807.01464/) (2018)
- [A Framework to Assess Value of Information in Future Vehicular Networks](https://mlsanity.com/paper/1905.09015/) (2019)

## Built on by (most cited first, 6 of 6)

- [SELMA: SEmantic Large-scale Multimodal Acquisitions in Variable Weather, Daytime and Viewpoints](https://mlsanity.com/paper/2204.09788/) (2022)
- [A Reinforcement Learning Framework for PQoS in a Teleoperated Driving Scenario](https://mlsanity.com/paper/2202.01949/) (2022)
- [Artificial Intelligence in Vehicular Wireless Networks: A Case Study Using ns-3](https://mlsanity.com/paper/2203.05449/) (2022)
- [Towards Decentralized Predictive Quality of Service in Next-Generation Vehicular Networks](https://mlsanity.com/paper/2302.11268/) (2023)
- [Teleoperated Driving: a New Challenge for 3D Object Detection in Compressed Point Clouds](https://mlsanity.com/paper/2506.11804/) (2025)
- [PRATA: A Framework to Enable Predictive QoS in Vehicular Networks via Artificial Intelligence](https://mlsanity.com/paper/2507.14211/) (2025)

## Abstract (arXiv)

Fully autonomous driving systems require fast detection and recognition of sensitive objects in the environment. In this context, intelligent vehicles should share their sensor data with computing platforms and/or other vehicles, to detect objects beyond their own sensors' fields of view. However, the resulting huge volumes of data to be exchanged can be challenging to handle for standard communication technologies. In this paper, we evaluate how using a combination of different sensors affects the detection of the environment in which the vehicles move and operate. The final objective is to identify the optimal setup that would minimize the amount of data to be distributed over the channel, with negligible degradation in terms of object detection accuracy. To this aim, we extend an already available object detection algorithm so that it can consider, as an input, camera images, LiDAR point clouds, or a combination of the two, and compare the accuracy performance of the different approaches using two realistic datasets. Our results show that, although sensor fusion always achieves more accurate detections, LiDAR only inputs can obtain similar results for large objects while mitigating the burden on the channel.