With the success of the 3D deep learning models, various perception technologies for
autonomous driving have been developed in the LiDAR domain. While these models …

Road detection is essential in autonomous driving, but conventional camera-based road
segmentation is resource-intensive and slow. The emergence of event cameras presents a …

Vehicle detection plays a critical role in autonomous driving, where two central sensing
modalities are lidar and radar. Although many deep neural network (DNN)-based methods …

Light detection and ranging (LiDAR) has been widely used in autonomous vehicles for
perception and localization. However, the cost of a high-resolution LiDAR is still prohibitively …

Annotation is a crucial component of point cloud analysis. However, due to the sheer
number of points in large-scale static point clouds, it is an expensive and time-consuming …

Road surface geometry provides information about navigable space in autonomous driving.
Ground plane estimation is done on “road” points after semantic segmentation of three …

Methods based on 64-beam LiDAR can provide very precise 3D object detection. However,
highly accurate LiDAR sensors are extremely costly: a 64-beam model can cost …

Road information is a major part of feature information of the Gaofen-3 (GF-3) synthetic
aperture radar (SAR) images, which has a great effect on path planning and intelligent …

This thesis deals with 3D object detection for autonomous driving, using various data
sources. The main objective is to detect 3D objects in driving scenes. There are a number of …

In recent years, graph signal processing (GSP) has attracted considerable attention due to
its ability to process high-dimensional data more efficiently. However, in many cases, the …