Learning and Analysis of AusRAP Attributes from Digital Video Recording for Road Safety The manual systems for road safety are inefficient, very time consuming and prone to error. Automated systems face many challenges in detecting all road safety objects accurately and accurately measuring the distance between these objects. Therefore, in this paper, a novel approach is proposed to analyze roadside video data in order to assess important attributes pertaining to road safety. The specific aim is to learn and analyse Australian Road Assessment Program (AusRAP) attributes obtained from digital video recordings (DVRs). In this work, a technique for segmentation and classification based on Fully Convolutional Network (FCN) is investigated. The proposed approach takes video frames as input, extracts features through convolutional layer and learns them to classify in fully connected layer. The novelty in our approach is learning road attributes and its application in road safety. The proposed approach is optimized using multiple image sizes and training epochs. The 411 images obtained from roadside video data provided by the Department of Transport and Main Roads (DTMR), Queensland, Australia have been used to analyze the proposed technique. The evaluation results are discussed, and a comparative analysis is presented.

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.