Early Recognition of Driving Intention for Lane Change Based on Recurrent Hidden Semi-Markov Model

Timely recognition of driving intention is crucial in the design of a safe and effective driving assistance system. This study proposes an efficient recognition approach based on Nonlinear Polynomial Regression (NPR) and Recurrent Hidden Semi-Markov Model (R-HSMM) to recognize the driver lane-change intention accurately in the early stage. The NPR model is utilized to transform the input signal amplitude into the standard form in order to improve system adaptability. Besides, an unsupervised time-series segmentation method named the Toeplitz Inverse Covariance-based Clustering (TICC) is applied to label the driving data automatically. The R-HSMM is utilized as a time-series classifier to classify the driving intention during the lane-change process into predefined categories based on the signals processed by the NPR. The proposed method is verified by the experiments with a driving simulator. The experimental results show that the proposed method can recognize driver intention earlier than the popular recognition methods, and also can reduce the number of false warnings during the lane-change process, which has great significance for driving safety improvement. Moreover, the proposed method can adapt well to various vehicle speeds achieving stable recognition performance.