Automated extraction of roads from remotely sensed data come forth various usages ranging
from digital twins for smart cities, intelligent transportation, urban planning, autonomous …

Autonomous vehicles were experiencing rapid development in the past few years. However,
achieving full autonomy is not a trivial task, due to the nature of the complex and dynamic …

Autonomous vehicles are expected to play a key role in the future of urban transportation
systems, as they offer potential for additional safety, increased productivity, greater …

In this work, a deep learning approach has been developed to carry out road detection by
fusing LIDAR point clouds and camera images. An unstructured and sparse point cloud is …

Road detection from the perspective of moving vehicles is a challenging issue in
autonomous driving. Recently, many deep learning methods spring up for this task, because …

In this work, a deep learning approach has been developed to carry out road detection using
only LIDAR data. Starting from an unstructured point cloud, top-view images encoding …

Despite rapid developments in visual image-based road detection, robustly identifying road
areas in visual images remains challenging due to issues like illumination changes and …

This paper addresses the problem of road scene segmentation in conventional RGB images
by exploiting recent advances in semantic segmentation via convolutional neural networks …

There are many sensor fusion frameworks proposed in the literature using different sensors
and fusion methods combinations and configurations. More focus has been on improving …

Autonomous driving requires 3D perception of vehicles and other objects in the in
environment. Much of the current methods support 2D vehicle detection. This paper …