In cooperative, connected, and automated mobility (CCAM), the more automated vehicles
can perceive, model, and analyze the surrounding environment, the more they become …

This article provides a review of the production and uses of maps for autonomous driving
and a synthesis of the opportunities and challenges. For many years, maps have helped …

In this paper, we present a system to train driving policies from experiences collected not just
from the ego-vehicle, but all vehicles that it observes. This system uses the behaviors of …

The rapid development of autonomous driving and mobile mapping calls for off-the-shelf
LiDAR SLAM solutions that are adaptive to LiDARs of different specifications on various …

Humans can orient themselves in their 3D environments using simple 2D maps. Differently,
algorithms for visual localization mostly rely on complex 3D point clouds that are expensive …

Autonomous vehicles commonly rely on highly detailed birds-eye-view maps of their
environment, which capture both static elements of the scene such as road layout as well as …

Autonomous navigation requires structured representation of the road network and instance-
wise identification of the other traffic agents. Since the traffic scene is defined on the ground …

For robots to navigate and interact more richly with the world around them, they will likely
require a deeper understanding of the world in which they operate. In robotics and related …

Robust and accurate, map-based localization is crucial for autonomous mobile systems. In
this paper, we exploit range images generated from 3D LiDAR scans to address the problem …

Localization and mapping are key capabilities of autonomous systems. In this paper, we
propose a modified Siamese network to estimate the similarity between pairs of LiDAR …