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Stanley: The Robot That Won the DARPA Grand Challenge

Authors
Sebastian Thrun, Mike Montemerlo, Hendrik Dahlkamp, David Stavens, Andrei Aron, James Diebel, Philip Fong, John Gale, Morgan Halpenny, Gabriel Hoffmann, Kenny Lau, Celia Oakley, Mark Palatucci, Vaughan 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 Ettinger, Adrian Kaehler, Ara Nefian, Pamela Mahoney
Year
2006
Journal Paper
Algorithm
Stanley
Source
Journal of Field Robotics
Identifiers
DOI 10.1002/rob.20147

Abstract

This article describes the robot Stanley, which won the 2005 DARPA Grand Challenge. Stanley was developed for high-speed desert driving without manual intervention. The robot's software system relied predominately on state-of-the-art artificial intelligence technologies, such as machine learning and probabilistic reasoning. This paper describes the major components of this architecture, and discusses the results of the Grand Challenge race.

Summary

Describes the complete autonomy stack behind Stanley, the winning vehicle in the DARPA Grand Challenge, with emphasis on perception, probabilistic terrain assessment, planning, and control integration. The paper is useful as a systems-level snapshot of how early autonomous driving combined machine learning with robust engineering constraints.

Knowledge Base

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External

Document DOI Google Scholar OpenAlex robots.stanford.edu onlinelibrary.wiley.com

Tags

Autonomous driving Autonomous navigation Autonomous vehicles DARPA urban challenge Probabilistic robotics Machine learning Robotics systems

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