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
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Autonomous Driving in Urban Environments: Boss and the Urban Challenge.
Chris Urmson,Joshua Anhalt,Drew Bagnell,Christopher R. Baker,Robert Bittner,Michael Clark,John M. Dolan,D Duggins,Tugrul Galatali,Christopher Geyer,Michele Gittleman,Sam Harbaugh,Martial Hebert,Thomas M. Howard,Sascha Kolski,Alonzo Kelly,Maxim Likhachev,Matthew McNaughton,Nick Miller,Kevin Peterson,Brian Pilnick,Ragunathan Rajkumar,Paul E. Rybski,Bryan Salesky,Young-Woo Seo,Sanjiv Singh,Jarrod M. Snider,Anthony Stentz,William Whittaker,Ziv Wolkowicki,Jason Ziglar,Hong Bae,Thomas G. Brown,Daniel Demitrish,Bakhtiar Brian Litkouhi,Jim Nickolaou,Varsha Sadekar,Wende Zhang,Joshua Struble,Michael Taylor,Michael Darms,Dave Ferguson +41 more
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Autonomous driving in urban environments: Boss and the Urban Challenge
Chris Urmson,Joshua Anhalt,Drew Bagnell,Christopher R. Baker,Robert Bittner,Michael Clark,John M. Dolan,D Duggins,Tugrul Galatali,Christopher Geyer,Michele Gittleman,Sam Harbaugh,Martial Hebert,Thomas M. Howard,Sascha Kolski,Alonzo Kelly,Maxim Likhachev,Matthew McNaughton,Nick Miller,Kevin Peterson,Brian Pilnick,Ragunathan Rajkumar,Paul E. Rybski,Bryan Salesky,Young-Woo Seo,Sanjiv Singh,Jarrod M. Snider,Anthony Stentz,William Whittaker,Ziv Wolkowicki,Jason Ziglar,Hong Bae,Thomas G. Brown,Daniel Demitrish,Bakhtiar Brian Litkouhi,Jim Nickolaou,Varsha Sadekar,Wende Zhang,Joshua Struble,Michael Taylor,Michael Darms,Dave Ferguson +41 more
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References
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Proceedings ArticleDOI
Self-supervised Monocular Road Detection in Desert Terrain
TL;DR: This method for identifying drivable surfaces in difficult unpaved and offroad terrain conditions as encountered in the DARPA Grand Challenge robot race achieves robustness by combining sensor information from a laser range finder, a pose estimation system and a color camera.
Proceedings ArticleDOI
A task description language for robot control
Reid Simmons,David Apfelbaum +1 more
TL;DR: TDL is an extension of C++ that provides syntactic support for task decomposition, synchronization, execution monitoring, and exception handling, and a compiler transforms TDL into pure C++ code that utilizes a platform-independent task management library.
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Rapidly adapting machine vision for automated vehicle steering
Dean A. Pomerleau,Todd Jochem +1 more
TL;DR: The Ralph vision system helps automobile drivers steer, by sampling an image, assessing the road curvature, and determining the lateral offset of the vehicle relative to the lane center.
Proceedings ArticleDOI
Vision-guided flight stability and control for micro air vehicles
TL;DR: This paper motivate the use of computer vision for MAV autonomy, arguing that given current sensor technology, vision may be the only practical approach to the problem, and describes the statistical vision-based horizon detection algorithm, which has been demonstrated at 30 Hz with over 99.9% correct horizon identification.
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SCARF: a color vision system that tracks roads and intersections
J.D. Crisman,Charles E. Thorpe +1 more
TL;DR: The SCARF system is described in detail, results on a variety of images are presented, and Navlab test runs usingSCARF are discussed.
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