In autonomous vehicles (AVs), LiDAR point cloud data are an important source to identify various obstacles present in the environment. The labeling techniques that currently available based on pixel-wise segmentation and bounding boxes detect each object road. However, Avs’ decision motion control trajectory path planning depends interaction among objects ability of Avs understand moving non-mo...

Abstract With artificial intelligence continuing to change people’s everyday life in profound ways, the desire endow vehicles with ability drive autonomously has emerged for years. Thus, autonomous driving become a popular field. The task can be divided into three general procedures: perception, planning, and locomotion. first foremost part of these procedures is perception task. Among those me...

In view of the fact that road network detection effect in high-resolution image is not satisfying, an approach for road network detection based on Bayesian Network is put forward in this paper. First, under the guidance of existing GIS data, extract roads from remote-sensing images, and obtain most of the unchanged road edge information and suspected road edge information. Then, making use of t...

Abstract Road network detection is critical to enhance disaster response and detecting a safe evacuation route. Due expanding computational capacity, road extraction from aerial imagery has been investigated extensively in the literature, specifically last decade. Previous studies have mainly proposed methods based on pixel classification or image segmentation as road/non-road images, such thre...

Traffic congestion is a situation on road networks that occurs as road use increases. When traffic demand increase, the interaction between vehicles slows the speed of the traffic stream and congestion occurs. As demand approaches the capacity of a road, extreme traffic congestion sets in. Current techniques for road-traffic monitoring rely on sensors which have limited capabilities, inflexibil...

This paper proposes a segmentation algorithm by means of an evidential reasoning to segment moving vehicles in front of the moving our car in a road traffic scene. Generally, an evidential reasoning finds the perceptually known evidences of a target and updates a probabilistic expectation for the target to be in an image. Since a noise image produces unreliable features and degrades the detecti...

Road scene segmentation is important in computer vision for different applications such as autonomous driving and pedestrian detection. Recovering the 3D structure of road scenes provides relevant contextual information to improve their understanding. In this paper, we use a convolutional neural network based algorithm to learn features from noisy labels to recover the 3D scene layout of a road...