Synthetic Driving Conditions Data Generation Using Federated Generative Adversarial Networks

This is the authors' abstract. We don't add key points for this paper.

Road safety remains a global challenge, especially in scenarios where behavioral and environmental factors heavily influence drivers' decision-making. Machine learning models play a crucial role in enhancing safety and informed decision-making by learning effective actions based on traffic conditions. However, training these models requires access to user data, which can compromise drivers' privacy and expose sensitive information. To address this issue, this study proposes a solution for generating synthetic driving condition data using a Federated Learning approach combined with Generative Adversarial Networks (GAN). This method enables model training across multiple federated learning clients, preserving data privacy by avoiding direct data sharing. By leveraging the Harmony dataset, similarity metrics such as Euclidean Distance and KL-Divergence were integrated into the GAN loss function to improve the quality of the generated synthetic data. The results demonstrate that the proposed approach successfully generates realistic driving condition data, supporting centralized model training while maintaining user privacy, showcasing its potential in privacy-conscious road safety applications.