Most autonomous vehicles (AVs) rely on LiDAR and RGB camera sensors for perception.
Using these point cloud and image data, perception models based on deep neural nets …

The core task of any autonomous driving system is to transform sensory inputs into driving
commands. In end-to-end driving, this is achieved via a neural network, with one or multiple …

The pursuit of autonomous driving relies on developing perception systems capable of
making accurate, robust, and rapid decisions to interpret the driving environment effectively …

Light Detection and Ranging (LiDAR) data collected by mobile mapping systems (MMS)
have been utilized to detect lane markings through intensity-based approaches. As LiDAR …

In this paper, we propose a simple and general framework for self-supervised point cloud
representation learning. Human beings understand the 3D world by extracting two levels of …

Simulators play a crucial role in autonomous driving, offering significant time, cost, and labor
savings. Over the past few years, the number of simulators for autonomous driving has …

Siamese trackers have recently achieved remarkable performance in visual tracking
community. However, they mainly utilize features from a certain convolutional layer for …

Railway transportation safety relies on the accurate location, detection, and measurement of
rail surface defects. However, the absence of image and point cloud fusion methods to …

Transformer architectures have exhibited promising performance in various autonomous
driving applications in recent years. On the other hand, its dedicated hardware acceleration …

LiDAR-Camera fusion-based 3D object detection is one of the main visual perception tasks
in autonomous driving, facing the challenges of small targets and occlusions. Image …