During last decades, owing to the increase in a number of vehicles, the rate of accident occurrence grows significantly. Efforts must be made to provide efficient tools to prioritize segments requiring safety improvement and identify influential factors on accidents. This objective of the research was to determine the safety oriented threshold of International Roughness Index (IRI) to recognize...

This paper presents the FPGA design of a convolutional neural network (CNN) based road segmentation algorithm for real-time processing of LiDAR data. For autonomous vehicles, it is important to perform road segmentation and obstacle detection such that the drivable region can be identified for path planning. Traditional road segmentation algorithms are mainly based on image data from cameras, w...

This paper deals with road extraction of high resolution aerial images of suburban scenes based on segmentation using the Normalized Cuts algorithm. The aim of our project is the extraction of roads for the assessment of a road database, however, this paper is restricted to road extraction. The segmentation as our basic step is designed to yield a good division between road areas and the surrou...

The uses of road map in daily activities are numerous but it is a hassle to construct and update a road map whenever there are changes. In Universiti Malaysia Sarawak, research on Automatic Road Extraction (ARE) was explored to solve the difficulties in updating road map. The research started with using Satellite Image (SI), or in short, the ARE-SI project. A Hybrid Simple Colour Space Segmenta...

This paper presents an efficient method for road signs segmentation in color images. Color segmentation of road signs is a difficult task due to variations in the image acquisition conditions. Therefore, a color constancy algorithm is usually applied prior to segmentation, which increases the computation time. The proposed method is based on a log-chromaticity color space which shows good invar...

In this paper, we address the problem of road segmentation and free space detection in the context of autonomous driving. Traditional methods either use 3-dimensional (3D) cues such as point clouds obtained from LIDAR, RADAR or stereo cameras or 2-dimensional (2D) cues such as lane markings, road boundaries and object detection. Typical 3D point clouds do not have enough resolution to detect fi...

Robust and quick road curb detection under various situations is critical in developing intelligent vehicles. However, the road curb detection is easily affected by the obstacles in the road area when Lidar based method is applied. A practical road curb detection method using point cloud from a three-dimensional Lidar for autonomous vehicle is reported in this paper. First, a multi-feature, loo...

The concept of an algorithm developed for the segmentation of a road border from the content of an image produced by TV camera mounted on a moving vehicle is presented. The extraction of a road boundary is an important step in the context of autonomous vehicle guidance. The segmentation algorithm combines statistical texture descriptors and the ones based on gray level co-occurrence matrix.

Abs t rac t . This paper presents a method I for tracking and segmenting vehicles in a traffic scene. The approach is based on a frame to frame segmentation followed by a tracking process. As opposed to usual segmentation methods, our method feedbacks segmentation with tracking information for improving results. Several facilities are provided for traffic monitoring such as vehicles trajectorie...