A Federated Learning Framework for Automated Decision Making with Microscopic Traffic Simulation

In recent years, exploring autonomous vehicles has become an emerging research topic due to the massive opportuni-ties to deploy deep learning models in real-world transportation applications. Although deep learning models have been widely applied to microscopic traffic simulations (MTS), they are limited to offline prescriptive analysis or predictive analysis. However, dynamics of a traffic environment frequently change over time; little research has been focused on dynamically updating deep learning models parameters in response to real-time traffic conditions. Further, for dynamic model parameter updates, it is difficult to preserve privacy since raw data is transmitted to the server for model updating (training). In this paper, we present a federated multimodal microscopic traffic simulation (FedMMTS) framework that incorporates federated learning to preserve data privacy and applies multimodal data analytics to achieve a more accurate sensing of the environment. We present preliminary experiments to demonstrate the effectiveness of this framework in a microscopic traffic environment. To the best of our knowledge, this is the first work that incorporates federated learning and data multimodality in MTS environments.