Autonomous vehicles (AV) are expected to improve, reshape, and revolutionize the future of
ground transportation. It is anticipated that ordinary vehicles will one day be replaced with …

Recent advancements in perception for autonomous driving are driven by deep learning. In
order to achieve robust and accurate scene understanding, autonomous vehicles are …

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 …

An autonomous vehicle (AV) requires an accurate perception of its surrounding environment
to operate reliably. The perception system of an AV, which normally employs machine …

We address semantic segmentation of road-objects from 3D LiDAR point clouds. In
particular, we wish to detect and categorize instances of interest, such as cars, pedestrians …

This paper presents an object classification method for vision and light detection and
ranging (LIDAR) fusion of autonomous vehicles in the environment. This method is based on …

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 …

It is vital that autonomous vehicles acquire accurate and real-time information about objects
in their vicinity, which fully guarantees the safety of the passengers and vehicle in various …

Abstract Machine learning (ML) plays the core function to intellectualize the transportation
systems. Recent years have witnessed the advent and prevalence of deep learning which …