Enhancing Vehicle Environmental Awareness via Federated Learning and Automatic Labeling

TL;DR

This paper proposes a federated learning framework with automatic labeling to improve vehicle identification in images, addressing privacy concerns and labeling challenges in vehicle environmental awareness systems.

Contribution

It introduces a novel combination of federated learning and automatic labeling techniques for vehicle identification, enhancing privacy and reducing labeling effort.

Findings

Feasibility demonstrated through experiments

Effective vehicle identification without sharing raw image data

Improved privacy preservation in vehicle data collection

Abstract

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.