Transfer Learning for Driver Model Adaptation via Modified Local Procrustes Analysis

A new driver model adaptation (DMA) method is proposed in this paper to help the model adaptation between different individual drivers. This method is based on transfer learning which can improve the DMA process at data level. The Gaussian mixture model (GMM)-based method is used to model the steering behaviour of drivers during the overtaking manoeuvre. Based on the GMM model, an alignment-based transfer learning technique named local Procrustes analysis (LPA) is modified to formulate the transfer learning problem for driver steering behaviour. A series of experiments based on the data collected from a driving simulator are carried out to evaluate the proposed modified LPA (MLPA). The experimental results verify the ability of MLPA for knowledge transfer. Compared with the GMM-only method and LPA, MLPA shows better performance on the prediction accuracy with much lower predicting errors in most cases.