FedPRM: Federated Personalized Mixture Representation for Driver Intention Prediction

Driver intention prediction has the potential to greatly improve the ability of autonomous vehicles (AVs) to effectively handle risky driving behaviors, thereby ensuring driving safety. Conventional data-driven approaches for driver intention prediction models typically involve gathering extensive driver-related data, which raises significant privacy concerns. With the development of the Internet of Vehicles (IoV), federated learning (FL) has emerged as a prominent privacy-preserving learning paradigm, garnering considerable attention. However, FL encounters challenges in driver intention prediction due to the heterogeneity of driver client data and the limited computational resources of vehicles. To address these challenges, this paper proposes the FedPMR framework, comprising a computationally efficient model for predicting driver intentions. Moreover, to tackle the problem of data heterogeneity, it leverages personalized mixture representation to provide a personalized model adapted to the local data distribution of each driver client. We conducted extensive experiments on the Brain4Cars dataset, achieving an F1-score of 95.24% and a comprehensive evaluation metric of 0.9663, exceeding state-of-the-art. The experimental results demonstrate that the proposed FedPMR effectively addresses the challenges encountered when applying FL to driver intention prediction.