We propose a new approach for real time inference of occupancy maps for self-driving cars
using deep neural networks (DNN) named NeuralMapper. NeuralMapper receives LiDAR …

We propose a new approach for real time inference of occupancy maps for self-driving cars
using deep neural networks (DNN) named NeuralMapper. NeuralMapper receives LiDAR …

The localization of self-driving cars is needed for several tasks such as keeping maps
updated, tracking objects, and planning. Localization algorithms often take advantage of …

In this work, we present a visual global localization system based on Deep Neural Networks
(DNNs) for self-driving cars, called DeepVGL (Deep Visual Global Localization), which …

Lane-level self-localization is essential for autonomous driving. Point cloud maps are
typically used for self-localization but are known to be redundant. Deep features produced …

Autonomous vehicles integrate complex software stacks for realizing the necessary iterative
perception, planning, and action operations. One of the foundational layers of such stacks is …

In this work, we present a visual global localization system based on Deep Neural Networks
(DNNs) for self-driving cars, named DeepVgl (Deep Visual Global Localization). In training …

This work proposes novel techniques forbuilding grid maps of large-scale environments,
and for estimating the localization of self-driving cars in these maps. The mapping technique …

Accuracy and time efficiency are two essential requirements for the self-localization of
autonomous vehicles. While the observation range considered for simultaneous localization …

The research on autonomous driving is expanding, and self-driving technology has the
potential of transforming not only the transportation system, but our whole society. The ability …