LAUSR

Accurately recognizing braking intensity levels (BIL) of drivers is important for guaranteeing the safety and avoiding traffic accidents in intelligent transportation systems. In this paper, an instance-level transfer learning (TL) framework is proposed to recognize BIL for a new driver with insufficient driving data by combining Gaussian Mixture Model (GMM) and importance weighted least squares probabilistic classifier (IWLSPC). By considering the statistic distribution, GMM is applied to cluster the data of braking behaviors into three levels with different intensities. With the density ratio calculated by unconstrained least-square importance fitting (ULSIF), LSPC is modified as IWLSPC to transfer the knowledge from one driver to another and recognize BIL for a new driver with insufficient driving data. Comparative experiments with non-transfer methods indicate that the proposed framework obtains a higher accuracy in recognizing BIL in the car following scenario, especially when sufficient data are not available.