# Enhancing Vehicle Environmental Awareness via Federated Learning and Automatic Labeling

arXiv: https://arxiv.org/abs/2408.12769 · HTML: https://mlsanity.com/paper/2408.12769/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 23 Aug 2024.

## Builds on (most cited first, 10 of 10)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [End-to-end Learning of Driving Models from Large-scale Video Datasets](https://mlsanity.com/paper/1612.01079/) (2016)
- [RODNet: A Real-Time Radar Object Detection Network Cross-Supervised by Camera-Radar Fused Object 3D Localization](https://mlsanity.com/paper/2102.05150/) (2021)
- [A Survey on Machine Learning Techniques for Auto Labeling of Video, Audio, and Text Data](https://mlsanity.com/paper/2109.03784/) (2021)
- [SLPC: a VRNN-based approach for stochastic lidar prediction and completion in autonomous driving](https://mlsanity.com/paper/2102.09883/) (2021)
- [Auto-Annotation Quality Prediction for Semi-Supervised Learning with Ensembles](https://mlsanity.com/paper/1910.13988/) (2019)

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

- [Argos: A Decentralized Federated System for Detection of Traffic Signs in CAVs](https://mlsanity.com/paper/2508.12712/) (2025)

## Abstract (arXiv)

Vehicle environmental awareness is a crucial issue in improving road safety. Through a variety of sensors and vehicle-to-vehicle communication, vehicles can collect a wealth of data. However, to make these data useful, sensor data must be integrated effectively. This paper focuses on the integration of image data and vehicle-to-vehicle communication data. More specifically, our goal is to identify the locations of vehicles sending messages within images, a challenge termed the vehicle identification problem. In this paper, we employ a supervised learning model to tackle the vehicle identification problem. However, we face two practical issues: first, drivers are typically unwilling to share privacy-sensitive image data, and second, drivers usually do not engage in data labeling. To address these challenges, this paper introduces a comprehensive solution to the vehicle identification problem, which leverages federated learning and automatic labeling techniques in combination with the aforementioned supervised learning model. We have validated the feasibility of our proposed approach through experiments.