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Abstract

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During high-intensity rainfall, hydroplaning is likely and can affect driving safety. Studies have indicated that the risk of hydroplaning increases with the increase in the water film depth that is dependent on surface texture properties, flow path slope, flow path length, rainfall intensity, and pavement surface type. However, little research work has been conducted to investigate pavement surface drainage at network levels because the existing data acquisition systems cannot continuously measure related data sets at high speeds. In the presented study, texture data were continuously collected at high speeds with the emerging 1-mm three-dimensional (3-D) PaveVision3D Ultra technology. The cross slope and longitudinal grade data were acquired with an inertial measurement unit system. Data from two rigid pavements constructed with dragged and grooved surface texture were used in this study. The analysis of variance test and the multifactor treatment statistical method were used to investigate the factors that influence the calculation of water film depth. Texture properties and flow path slope were determined to be more significant for surface drainage capacity than was flow path length. The widely used PAVDRN model was used to calculate hydroplaning speed, and the potential hydroplaning performance of the test sites was evaluated. The significance of the presented data is twofold. First, it integrates the real-time 1-mm 3-D surface data and inertial measurement unit system data into a hydroplaning speed prediction model. Second, this method can identify hazardous locations where there is hydroplaning so that pavement engineers may take remedial measures, such as constructing superior grooving texture or posting appropriate traffic speed signs, to decrease hydroplaning potential and minimize traffic accidents.

Lane marking detection and localization are crucial for autonomous driving and lane-based pavement surveys. Numerous studies have been done to detect and locate lane markings with the purpose of advanced driver assistance systems, in which image data are usually captured by vision-based cameras. However, a limited number of studies have been done to identify lane markings using high-resolution laser images for road condition evaluation. In this study, the laser images are acquired with a digital highway data vehicle (DHDV). Subsequently, a novel methodology is presented for the automated lane marking identification and reconstruction, and is implemented in four phases: (1) binarization of the laser images with a new threshold method (multi-box segmentation based threshold method); (2) determination of candidate lane markings with closing operations and a marching square algorithm; (3) identification of true lane marking by eliminating false positives (FPs) using a linear support vector machine method; and (4) reconstruction of the damaged and dash lane marking segments to form a continuous lane marking based on the geometry features such as adjacent lane marking location and lane width. Finally, a case study is given to validate effects of the novel methodology. The findings indicate the new strategy is robust in image binarization and lane marking localization. This study would be beneficial in road lane-based pavement condition evaluation such as lane-based rutting measurement and crack classification.