Classification of Driving Postures by Support Vector Machines

The objective of this study is to investigate different pattern classification paradigms in the automatically understanding and characterizing driver behaviors. With features extracted from a driving posture dataset consisting of grasping the steering wheel, operating the shift lever, eating a cake and talking on a cellular phone, created at Southeast University, holdout and cross-validation experiments on driving posture classification are firstly conducted using Support Vector Machines (SVMs) with five different kernels, and then comparatively conducted with other four commonly used classification methods including linear perception classifier, k-nearest neighbor classifier, Multi-layer perception classifier, and parzen classifier. The holdout experiments show that the intersection kernel outperforms the other four kernels, and the SVMs with intersection kernel offers better classification rates and best real-time quality among the five classifiers, which shows the effectiveness of the proposed feature extraction method and the importance of SVM classifier in automatically understanding and characterizing driver behaviors towards human-centric driver assistance systems.