__________________________________… Intelligence (AI), Internet of Things (IoT),
embedded systems, and control elf-driving cars are an active area of interdisciplinary …

Effective detection of road objects in diverse environmental conditions is a critical
requirement for autonomous driving systems. Multi-modal sensor fusion is a promising …

This paper proposes a SEG-VoxelNet that takes RGB images and LiDAR point clouds as
inputs for accurately detecting 3D vehicles in autonomous driving scenarios, which for the …

Three-dimensional object detection plays a key role in autonomous driving systems. The
performance of light detection and ranging (LiDAR)-based models is limited because of the …

The camera and lidar are significant sensors for automatic driving, they can provide
adequate complementary information. However, 3D point cloud object detection suffers from …

Nowadays, a large amount of labeled data is crucial for deep neural network training.
However, data labeling is still a time-and labor-consuming task, especially when labeling 3D …

In the field of Advanced Driver-Assistance Systems, road traffic actors detection is a vital task
in order to avoid human errors in driving. Unlike camera only-based convolutional neural …

Most of the existing road detection methods are either single-modal based, eg, based on
LiDAR or camera, or multi-modal based with LiDAR-camera fusion. The algorithms are …

Accurate 3D object detection is vital for autonomous driving since it facilitates accurate
perception of the environment through multiple sensors. Although cameras can capture …

In urban settings, roadside infrastructure LiDAR is a ground-based remote sensing system
that collects 3D sparse point clouds for the traffic object detection of vehicles, pedestrians …