Driver Monitoring-Based Lane-Change Prediction: A Personalized Federated Learning Framework

In order to enhance driving safety and identify potential hazards, next-generation intelligent vehicles will need to understand human drivers' intentions and predict their potential maneuvers correctly. In a lane-change scenario, a driver's head rotation measured by the in-cabin driver monitoring camera can serve as a reliable indicator to predict his/her intention. However, using a general model to predict each driver's maneuver is not accurate, while directly sharing the personalized monitoring data to other intelligent vehicles raises the privacy concern. In this paper, we propose a clustering-based personalized federated learning framework (CPFL) to predict lane-change maneuver based on driver monitoring data. Personalization is added on top of the traditional federated learning (FL) through clustering, which separates and groups similar driving behaviors based on clustering parameters: head position threshold and average pre-lane-change preparation time. Long-Short Term Memory (LSTM) networks with different sequence lengths are deployed to predict lane changes in different clusters based on the lane-change preparation time. CPFL framework is trained and tested using the data collected from several human drivers under different driving scenarios through the Unity simulation platform. According to the results, CPFL's average training efficiency is 7.6 times higher than the classic FedAvg approach, and CPFL also offers better adaptability to different driving behaviors than FedAvg with 4% higher accuracy, 0.2% fewer false positives, and 27.8% fewer false negatives.