This study focuses on changes in the friction characteristics of paved roads under various conditions from the viewpoint of traffic safety. In general, the braking characteristics of road vehicles are examined using the μ–s characteristics of the tires. Therefore, in our research, we used three types of limited datasets and identified them using the Magic Formula proposed by Prof. Pacejka. Based on various experiments, it was shown that changes in the road surface environment, such as dry and wet conditions, significantly affect the μ–s characteristics, and this influence varies significantly depending on the pavement conditions. In this study, as the first stage of the analysis of these influences, it was clarified that the difference in the friction characteristics of wet and dry road surfaces varies significantly depending on the pavement surface, which is based on experimental results obtained using actual roads. As this variation is closely related to the safety of actual road traffic, we used a brush model, which is a dynamic model of the road surface and tires, to clarify the differences in this characteristic.

This study focuses on the possibility of constructing a database on friction coefficients for actual roads from the viewpoint of traffic safety. A measurement algorithm is established to construct a road friction-measuring device. Next, the tires are selected for use in the measurements and their characteristics are measured using a bench tire characteristic tester. The measuring device is designed and constructed based on these characteristics. Finally, using this device, the measurement results of the road friction characteristics for two types of road surfaces are presented.

This paper deals with the possibility for construction of the database on friction coefficient on the actual roads from the viewpoint of traffic safety. A measurement algorithm is determined in order to construct road friction measuring device. Next, the tires used for measurement are selected and the characteristics are measured using bench tire characteristic tester, and the measuring device is designed and constructed based on the characteristics. Finally, using this device, the measurement results of the road friction characteristics for two types of road surfaces are shown.

Forward road information is essential to improve the safety of next-generation advanced driver assistance systems/automated driving systems. In this study, we developed a noncontact friction estimation system that integrates multivariate information from multiple sensors, including three-dimensional light detection and ranging, millimeter-wave radar, and infrared thermometers. We used the continuous peak μ measured by a proprietary friction measurement trailer as the ground truth which has demonstrated extremely high measurement accuracy (R² = 0.9987) on test road sections and similar surfaces. Through multivariate regression analysis using real road data, including snowy surfaces, the system achieved a high explanatory power with an adjusted coefficient of determination of 0.75. In addition, a time-series analysis of squared errors revealed that sensor fusion based on three physical factors, road roughness, moisture content, and thermal response resulted in the most accurate and robust estimation model.