Stanley: The Robot that Won the DARPA Grand Challenge
Sebastian Thrun,Michael Montemerlo,Hendrik Dahlkamp,David Stavens,Andrei Aron,James Diebel,Philip Fong,John Gale,Morgan Halpenny,Gabriel M. Hoffmann,Kenny Lau,Celia M. Oakley,Mark Palatucci,Vaughan R. Pratt,Pascal Stang,Sven Strohband,Cedric Dupont,Lars-Erik Jendrossek,Christian Koelen,Charles Markey,Carlo Rummel,Joe van Niekerk,Eric Jensen,Philippe Alessandrini,Gary Bradski,Bob Davies,Scott M. Ettinger,Adrian Kaehler,Ara V. Nefian,Pamela Mahoney +29 more
TLDR
The robot Stanley, which won the 2005 DARPA Grand Challenge, was developed for high‐speed desert driving without manual intervention and relied predominately on state‐of‐the‐art artificial intelligence technologies, such as machine learning and probabilistic reasoning.Abstract:
This article describes the robot Stanley, which won the 2005 DARPA Grand Challenge. Stanley was developed for high-speed desert driving without human intervention. The robot’s software system relied predominately on state-of-the-art AI technologies, such as machine learning and probabilistic reasoning. This article describes the major components of this architecture, and discusses the results of the Grand Challenge race.read more
Citations
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Journal ArticleDOI
Template-based autonomous navigation and obstacle avoidance in urban environments
TL;DR: A vehicle control system capable of learning behaviors based on examples from human driver and analyzing different levels of memory of the templates, which are an important capability to autonomous vehicle drive, is presented.
Journal ArticleDOI
Cloud‐based Real‐time Outsourcing Localization for a Ground Mobile Robot in Large‐scale Outdoor Environments
TL;DR: Experimental results show that the proposed cloud‐based real‐time outsourcing localization architecture could be applicable to large‐scale outdoor environments for autonomous robots in real time.
Proceedings ArticleDOI
How MuCAR won the convoy scenario at ELROB 2016
Carsten Fries,Patrick Burger,Jan Kallwies,Benjamin Naujoks,Thorsten Luettel,Hans-Joachim Wuensche +5 more
TL;DR: The hard- and software system of MuCAR, the new multi-sensor data fusion method as well as the challenging situations during the ELROB 2016 robotics trial are described aswell as the challenges faced during the competition.
DissertationDOI
Multimodal machine learning for intelligent mobility
TL;DR: This work demonstrates that mobile robots can use multimodal machine learning to derive driver policy and therefore make autonomous decisions and demonstrates that online learning mechanism is superior to one-off training of deep neural networks that require large datasets to generalize to unfamiliar surroundings.
Proceedings ArticleDOI
Realizing complex autonomous driving maneuvers the approach taken by team CarOLO at the DARPA urban challenge
J.M. Wille,T. Form +1 more
TL;DR: The approaches taken by team CarOLO for the realization of complex autonomous driving maneuvers and an efficient and flexible interface as well as the control structure of Carolinepsilas system is shown that was applied in the DARPA Urban Challenge.
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