Identification of RoadSurface Type Using Deep Neural Networks for Friction Coefficient Estimation Nowadays, vehicles have advanced driverassistance systems which help to improve vehicle safety and save the lives of drivers, passengers and pedestrians. Identification of the roadsurface type and condition in real time using a video image sensor, can increase the effectiveness of such systems significantly, especially when adapting it for braking and stabilityrelated solutions. This paper contributes to the development of the new efficient engineering solution aimed at improving vehicle dynamics control via the antilock braking system (ABS) by estimating friction coefficient using video data. The experimental research on three different road surface types in dry and wet conditions has been carried out and braking performance was established with a car mathematical model (MM). Testing of a deep neural networks (DNN)based roadsurface and conditions classification algorithm revealed that this is the most promising approach for this task. The research has shown that the proposed solution increases the performance of ABS with a rulebased control strategy.

Weather Data Integrated Mask RCNN for Automatic Road Surface Condition Monitoring Monitoring road surface conditions plays a crucial role in driving safety and road maintenance, especially in winter seasons. Traditional methodologies often employ manual inspection and expensive instruments, e.g., NIR cameras. However, image analysis based on normal cameras can provide an economical and efficient solution for road surface monitoring. This paper presents an automatic classification model of road surface conditions using a deep learning approach based on road images and weather measurement. A modified mask RCNN model has been developed by integrating weather data based on transfer learning. Experimental results with respect to manual judgment of road surface conditions have demonstrated very high accuracy of the developed model.