# Edge-Aided Sensor Data Sharing in Vehicular Communication Networks

arXiv: https://arxiv.org/abs/2206.08882 · HTML: https://mlsanity.com/paper/2206.08882/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 32 references that are in the map.
- First posted on arXiv 17 Jun 2022.

## Builds on (most cited first, 11 of 11)

- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehicles](https://mlsanity.com/paper/2103.03786/) (2021)
- [Automatic Extrinsic Calibration for Lidar-Stereo Vehicle Sensor Setups](https://mlsanity.com/paper/1705.04085/) (2017)
- [Targetless Rotational Auto-Calibration of Radar and Camera for Intelligent Transportation Systems](https://mlsanity.com/paper/1904.08743/) (2019)
- [Automatic Calibration of Dual-LiDARs Using Two Poles Stickered with Retro-Reflective Tape](https://mlsanity.com/paper/1911.00635/) (2019)
- [Distributed Multi-Target Tracking for Autonomous Vehicle Fleets](https://mlsanity.com/paper/2004.05965/) (2020)
- [Distributed Kalman filtering with minimum-time consensus algorithm](https://mlsanity.com/paper/1703.05438/) (2017)
- [Randomized Consensus based Distributed Kalman Filtering over Wireless Sensor Networks](https://mlsanity.com/paper/1810.02531/) (2018)
- [Sample Greedy Gossip for Distributed Network-Wide Average Computation](https://mlsanity.com/paper/1903.11531/) (2019)

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

- [Bridging the Domain Gap for Multi-Agent Perception](https://mlsanity.com/paper/2210.08451/) (2022)
- [V2XP-ASG: Generating Adversarial Scenes for Vehicle-to-Everything Perception](https://mlsanity.com/paper/2209.13679/) (2022)
- [Edge-Enabled Collaborative Object Detection for Real-Time Multi-Vehicle Perception](https://mlsanity.com/paper/2506.06474/) (2025)

## Abstract (arXiv)

Sensor data sharing in vehicular networks can significantly improve the range and accuracy of environmental perception for connected automated vehicles. Different concepts and schemes for dissemination and fusion of sensor data have been developed. It is common to these schemes that measurement errors of the sensors impair the perception quality and can result in road traffic accidents. Specifically, when the measurement error from the sensors (also referred as measurement noise) is unknown and time varying, the performance of the data fusion process is restricted, which represents a major challenge in the calibration of sensors. In this paper, we consider sensor data sharing and fusion in a vehicular network with both, vehicle-to-infrastructure and vehicle-to-vehicle communication. We propose a method, named Bidirectional Feedback Noise Estimation (BiFNoE), in which an edge server collects and caches sensor measurement data from vehicles. The edge estimates the noise and the targets alternately in double dynamic sliding time windows and enhances the distributed cooperative environment sensing at each vehicle with low communication costs. We evaluate the proposed algorithm and data dissemination strategy in an application scenario by simulation and show that the perception accuracy is on average improved by around 80 % with only 12 kbps uplink and 28 kbps downlink bandwidth.