Surface characteristics of asphalt pavement have significant impacts on road noise. However, the quantitative relationship between them is not completely clarified. The objective of this paper is to quantify the effect of asphalt pavement surface characteristics on road noise. Firstly, based on acoustics theory, we determined the pavement roughness (IRI) and texture depth (TD) as the factors affecting road noise. Secondly, we conducted a series of field tests, collecting the road noise inside and outside the automobile and measuring the IRI and TD. Finally, we statistically obtained the relationships among interior vehicle noise and exterior vehicle noise, the IRI and ID of the pavement. The result shows that road noise, neither inside nor outside the vehicle, is significant to the TD. Interior vehicle noise is linearly related to the IRI, which rises with the increase of IRI. In addition, exterior vehicle noise can be considered to have a quadric relationship with ln(IRI · TD).

Image segmentation is a crucial topic in image analysis and understanding, and the foundation of target detection and recognition. Image segmentation, essentially, can be considered as classifying the image according to the consistency of the region and the inconsistency between regions, it is widely used in medical and criminal investigation, cultural relic identification, monitoring and so forth. There are two outstanding common problems in the existing segmentation algorithm, one is the lack of accuracy, and the other is that it is not widely applicable. The main contribution of this paper is to present a novel segmentation method based on the information entropy theory and multi-scale transform contour constraint. Firstly, the target contour is initially obtained by means of a multi-scale sample top-hat and bottom-hat transform and an improved watershed method. Subsequently, in terms of this initial contour, the interesting areas can be finely segmented out with an innovative 3D flow entropy method. Finally, the sufficient synthetic and real experiments proved that the proposed algorithm can greatly improve the segmentation effect. In addition, it is widely applicable.

Tire-road noise contains two aspects, one is aerodynamic noise, another is vibration noise that the traffic noise affects a large proportion of it. It is obvious that reduction of vibration noise can greatly improve the quality of sound environment in town. The primary task of vibration noise reduction study is to get clear on the generation mechanism of driving vibration noise. According to previous researches, the contact conditions between tire and road surface have a significant effect on driving vibration noise. Especially, there must be a link, having not been rigorously studied, between the tire-road contact pressure and vibration noise. Based on the above, the relationship between driving vibration noise and tire-road contact pressure is established in this paper. Through field experiments, it proves that the driving vibration noise of micro-surface is higher than that of SMA-10 (stone matrix asphalt). Meanwhile, the movement progresses of wheel on these two different pavement surfaces and the contact pressure are simulated via DEM (discrete element model), which shows that the volatility of tire-road contact pressure of micro-surface is greater than that of SMA-10. Both the field experiment and DEM demonstrate that the higher volatility of tire-road contact pressure leads the higher driving vibration noise, which can serve as the theoretical foundation of follow-up study.