This paper presents a framework to build a joint 2-D-3-D traffic sign landmark data set for
geo-localization using mobile laser scanning (MLS) data. The MLS data include 3-D point …

Autonomous vehicles rely extensively on onboard sensors to perceive their surrounding
environments for motion planning and vehicle control. Despite recent advancements …

Autonomous driving has become a prominent topic with the rise of intelligent urban vision in
communities. Advancements in automated driving technology play a significant role in the …

Multimodal deep learning is about learning features over multiple modalities. Impressive
progress has been made in deep learning solutions that rely on a single sensor modality for …

We studied the use of a rotating multi-layer 3D Light Detection And Ranging (LiDAR) sensor
(specifically the Velodyne HDL-32E) mounted on a social robot for the estimation of features …

Strong demand for autonomous vehicles and the wide availability of 3D sensors are
continuously fueling the proposal of novel methods for 3D object detection. In this paper, we …

Traditionally, road detection approaches mostly capitalize on RGB images, 3D LiDAR point
cloud or their fusion. However, RGB camera is sensitive to light conditions, while LiDAR …

Multimodal data fusion is becoming a trend for the field of autonomous driving, especially for
lane detection. In the process of driving, sensors often encounter problems such as modality …

Robust perception in autonomous vehicles is a huge challenge that is the main tool for
detecting and tracking the different kinds of objects around the vehicle. The aim is to reach …

Vehicle speed estimation is one of the most critical issues in intelligent transportation system
(ITS) research, while defining distance and identifying direction have become an …