Autonomous vehicles rely on their perception systems to acquire information about their
immediate surroundings. It is necessary to detect the presence of other vehicles …

Automotive perception involves understanding the external driving environment and the
internal state of the vehicle cabin and occupants using sensor data. It is critical to achieving …

Nowadays, and more than a decade after the first steps towards autonomous driving, we
keep heading to achieve fully autonomous vehicles on our roads, with LiDAR sensors being …

In this paper, we introduce a deep encoder-decoder network, named SalsaNet, for efficient
semantic segmentation of 3D LiDAR point clouds. SalsaNet segments the road, ie drivable …

In the near future, autonomous vehicles with full self-driving features will populate our public
roads. However, fully autonomous cars will require robust perception systems to safely …

Ground plane estimation and ground point segmentation is a crucial precursor for many
applications in robotics and intelligent vehicles like navigable space detection and …

Environmental perception plays an essential role in autonomous driving tasks and demands
robustness in cluttered dynamic environments such as complex urban scenarios. In this …

In this work, we propose a novel voxel representation which allows for efficient, real-time
processing of point clouds with deep neural networks. Our approach takes a 2D …

Localization and mapping are key requirements for autonomous mobile systems to perform
navigation and interaction tasks. Iterative Closest Point (ICP) is widely applied for LiDAR …

In this paper, a slope-robust cascaded ground segmentation in 3D point cloud for
autonomous vehicles is presented. In many challenging terrains encountered by …