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The test is carried out to examine the vehicle interior noise, windscreen vibration and wiper blade vibration induced by wiper friction, under the combination conditions with various wiping speed and windscreen wetness. The noise's time-frequency characteristics, influence factors and noise source were approached by means of time domain, frequency domain and time-frequency domain analysis. The results indicated that wiper noise can be classified into reversal noise and wiping noise. The reversal noise is characterized by impulsive noise, and wiping noise is featured by wide-band noise with harmonic components. The nature of both types of noises is strongly affected by the windshield wetness; however, it is far less affected by the wiping speed. The wiping noise is mainly resulted from lateral and vertical vibration of wiper blades.

Low-frequency vibration caused by road roughness while driving is transmitted to the human body through tires, suspension, and seats. Prolonged exposure of the human body to the vibratory environment will have an impact on ride comfort or even health issues. In order to investigate the vibration response of various segments of occupants while driving, a 15-DOF multi-body dynamic model depicting the shanks with feet, thighs, pelvis, torso with arms, and the head of occupants is established in the two-dimensional sagittal plane, which considers the contact between the occupant and the cushion, backrest headrest, and the vehicle floor simultaneously. The biodynamic parameters are obtained by fitting the published vibration experimental data based on an optimization algorithm. The previously proposed half-car model is incorporated into the human model to construct an integrated vehicle-human model for further ride comfort analysis.

Driver behaviors provide abundant information and feedback for future Advanced Driver Assistance Systems (ADAS). Driver’s eye and head may present some typical movement patterns before executing driving maneuvers. It is possible to use driver’s head and eye movement information for predicting driver intention. Therefore, to determine the most important features related to driver intention has attracted widespread research interests. In this paper, a method to analyze the correlation between driver’s visual features and driver intention is proposed, aiming to determine the most representative features for driver intention prediction. Firstly, naturalistic driving experiment is conducted to collect driver’s videos during executing lane keeping and lane change maneuvers. Then, driver’s head and face visual features are extracted from those videos. By using boxplot and independent samples T-test, features which have significant correlation with driver intention are found.