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3D Perception and Planning for Self-Driving and Cooperative Automobiles
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BibTeX
@MISC{Stiller_3dperception,
author = {Christoph Stiller and Julius Ziegler},
title = {3D Perception and Planning for Self-Driving and Cooperative Automobiles},
year = {}
}
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Abstract
Abstract—This presentation focusses on key technologies for automobiles that perceive a priori unknown environment and automatically navigate through everyday traffic. Methods for 3D Machine perception based on lidar and video sensors are outlined. Beyond classical metrology, the recognition and basic understanding of situations must be accomplished for automated trajectory planning in urban traffic. We discuss how to represent and acquire metric, symbolic and conceptual knowledge from video and lidar data of a vehicle. A hardware and software ar-chitecture tailored to this knowledge structure for an autonomous vehicle is proposed. Emphasis is laid on methods for situation recognition employing geometrical and topological reasoning and Markov Logic Networks. A quality measure for trajectories is imposed that considers safety, efficiency, and comfort. We adopt a flat input parameterization to plan trajectories that optimize the imposed quality measure. Results from the autonomous vehicle AnnieWAY that recently won the Grand Cooperative Driving Challenge are shown in real world urban and platooning scenarios. I.
Keyphrases
cooperative automobile key technology lidar data imposed quality measure everyday traffic basic understanding urban traffic flat input parameterization real world urban quality measure beyond classical metrology priori unknown environment situation recognition autonomous vehicle software ar-chitecture conceptual knowledge markov logic network considers safety video sensor automated trajectory planning knowledge structure autonomous vehicle annieway topological reasoning grand cooperative driving challenge machine perception
