Skid resistance depends on the macro- and micro-textural characteristics of pavement surface. Mean profile depth (MPD) is a widely used two-dimensional macro-texture indicator calculated from a single pavement surface profile, while pavement micro-texture is primarily affected by the characteristics of aggregates contained within the surface. In this study, twenty-two pavement sites, constructed using six common types of preventive treatments and eight typical sources of aggregates in Oklahoma, are selected as the field test beds. Pavement skid resistance and surface macro-texture data are collected in parallel at highway speeds using a grip tester and a high speed texture profiler, while aggregate properties under 3D are measured using a portable ultra-high resolution 3D laser imaging scanner. MPD values are calculated to represent surface macro-texture. Thirty-one 3D aggregate parameters in four categories (textural, feature, height, and material ratio & volume parameters) are investigated to characterise micro-texture attributes. The Pearson correlation coefficient matrix is computed within each category to exclude the parameters with strong correlations and remove their potential multi-collinearity during friction model development. Multivariate analysis is subsequently performed to examine the relationship between pavement skid resistance, and surface and aggregate texture properties. Eight texture parameters have showed statistical significance on pavement friction. Entropy (T_etp textural parameter) and peak curvature (S_pc feature based parameter) present the most significant influences on pavement friction, while MPD demonstrates the least impact. The developed model based on surface and aggregate texture properties can better predict pavement friction performance. The comprehensive analysis of texture properties could assist highway agencies understanding the appropriate characterisation of aggregates and developing aggregate selection guideline for optimised skid resistance.

Skid resistance is an important surface characteristic that influences roadway safety. Various studies have been performed to understand the interaction between pavement and tires through numerical simulation for skid resistance prediction. However, the friction parameters required for simulation inputs are generally determined by objective assumptions. This paper develops a finite element method (FEM)-based skid resistance simulation framework using in-situ 3D pavement surface texture and skid resistance data. A 3D areal pavement model is reconstructed from high resolution asphalt pavement surface texture data. The exponential decay friction model is implemented in the simulation and the interface friction parameters required for the simulation are determined using the binary search back-calculation approach based on a trial process with the desired level of differences between simulated and observed skid numbers. To understand the influence of texture characteristics on interface friction parameters, the high-resolution 3D texture data is separated into macro- and micro-scales through Butterworth filtering and various areal texture indicators are calculated at both levels. Principal component analysis (PCA) regression analysis is conducted to quantify the relationship between various texture characteristics and the interface friction parameters. The results from this study can be used to better prepare the inputs of friction parameters for FEM simulation.

Many studies have concluded that pavement micro- and macro- texture characteristics have significant contributions to pavement friction and thus roadway safety. This study explores novel three-dimensional (3D) areal texture parameters to represent pavement texture characteristics at both micro- and macroscales and their usage for friction prediction. Three rounds of pavement friction and texture data were collected from 2015 to 2017 at a testing site in Oklahoma with six different mixture designs. Pavement friction was acquired by a Dynamic Friction Tester, and the corresponding texture data were collected by a portable ultrahigh-resolution 3D laser scanner. Each 3D texture datum is decomposed into micro- and macroscales with predesigned Butterworth filters. Twenty-seven 3D texture parameters falling into five categories are calculated at both texture levels. Subsequently, the random forest algorithm is implemented to determine the most important texture parameters for friction predictive model development. The selected macro- and micro-texture parameters account for 48.8 % and 39.6 % contributions to high-speed friction and 50.0 % and 14.1 % contributions to low-speed friction. The temperature during testing also exhibits a significant impact, with 11.6 % and 35.9 % contributions to high- and low-speed friction, respectively.

Maintaining desired pavement friction is an effective way to reduce the crash rate. Pavement texture plays an important role in tire-pavement friction performance. However, the contribution of texture at different depths or wavelengths to friction measurement has not been fully understood. This study conducts a multiresolution analysis using three-dimensional (3D) asphalt pavement texture data to correlate pavement texture to friction measurements. Between 2014 and 2018, multiple rounds of pavement texture and friction data were acquired on two field sites. The top-surface topography of 3D texture data is sliced with eight depths to explore the critical contact depth between pavement texture and tire rubber for friction testing. At each depth, 3D texture data is decomposed into sub-images via discrete wavelet transform to extract texture features at 11 wavelengths ranging from 0.1 mm to 102.4 mm. Subsequently, the wavelet energy of each wavelength is calculated as the texture indicator. Finally, the texture energy matrix at different wavelengths and depths along with the ambient temperature during friction testing are correlated with the measured friction data using cross-validation and step-wise multivariate linear regression. The pavement texture of wavelengths up to 3.2 mm at the top 2.5 mm of top-surface topography is identified as the critical contact zone for pavement-tire interaction.

Human factors are a primary cause of vehicle accidents. Driver monitoring systems, utilizing a range of sensors and techniques, offer an effective method to monitor and alert drivers to minimize driver error and reduce risky driving behaviors, thus helping to avoid Safety Critical Events (SCEs) and enhance overall driving safety. Artificial Intelligence (AI) tools, in particular, have been widely investigated to improve the efficiency and accuracy of driver monitoring or analysis of SCEs. To better understand the state-of-the-art practices and potential directions for AI tools in this domain, this work is an inaugural attempt to consolidate AI-related tools from academic and industry perspectives. We include an extensive review of AI models and sensors used in driver gaze analysis, driver state monitoring, and analyzing SCEs. Furthermore, researchers identified essential AI tools, both in academia and industry, utilized for camera-based driver monitoring and SCE analysis, in the market. Recommendations for future research directions are presented based on the identified tools and the discrepancies between academia and industry in previous studies. This effort provides a valuable resource for researchers and practitioners seeking a deeper understanding of leveraging AI tools to minimize driver errors, avoid SCEs, and increase driving safety.

Image-based systems are becoming popular to collect pavement condition data for pavement management activities. Pavement engineers define various distress categories based on pavement types. However, software solutions today have limitations in correctly recognizing pavement types from the collected images in an automated way. This paper presents a convolutional neural network (CNN)-based PvmtTPNet to automatically recognize pavement types at acceptable levels of consistency, accuracy, and high-speed. Pavement images on asphalt concrete pavements, jointed plain concrete pavements, and continuously reinforced concrete pavements in varying conditions were collected via the PaveVision3D system in 2018. A total number of 21,000 two-dimensional (2D) images were prepared, while 80% and 20% of them were randomly selected for training and testing. The CNN network included six layers with 992,979 tuned hyperparameters and achieved 99.85% and 98.37% prediction accuracies for training and testing in pavement type recognition. Images obtained from another two data collections in 2019 were used to validate the PvmtTPNet, and 91.27% and 96.66% prediction accuracies were reached, individually. In addition, the PvmtTPNet shows the highest precision, recall, and F1-score for asphalt concrete (AC) images, which is followed by jointed plain concrete pavement (JPCP) and continuously reinforced concrete pavement (CRCP) images. The developed methodology can provide substantial assistance toward a fully automated pavement condition data analysis for image-based systems, even though a near 100% accuracy is the final objective of the continuing research.