48 citations · 62 across the 3 of their papers we have counts for
4 papers
Deep Tracking on the Move: Learning to Track the World from a Moving Vehicle using Recurrent Neural Networks
Julie Dequaire, Dushyant Rao, Peter Ondruska +2
This paper presents an end-to-end approach for tracking static and dynamic objects for an autonomous vehicle driving through crowded urban environments. Unlike traditional approach…
Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks
Martin Engelcke, Dushyant Rao, Dominic Zeng Wang +2
This paper proposes a computationally efficient approach to detecting objects natively in 3D point clouds using convolutional neural networks (CNNs). In particular, this is achieve…
Watch This: Scalable Cost-Function Learning for Path Planning in Urban Environments
Markus Wulfmeier, Dominic Zeng Wang, Ingmar Posner
In this work, we present an approach to learn cost maps for driving in complex urban environments from a very large number of demonstrations of driving behaviour by human experts.…
End-to-End Tracking and Semantic Segmentation Using Recurrent Neural Networks
Peter Ondruska, Julie Dequaire, Dominic Zeng Wang +1
In this work we present a novel end-to-end framework for tracking and classifying a robot's surroundings in complex, dynamic and only partially observable real-world environments.…