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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Describing Composite Urban Workspaces
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Optimal motion planning by reinforcement learning in autonomous mobile vehicles
TL;DR: A new algorithm based on the cell-mapping techniques and reinforcement learning methods to obtain the optimal motion planning of a vehicle considering kinematics, dynamics and obstacle constraints is implemented and tested in real conditions.
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Ground Segmentation Based on Loopy Belief Propagation for Sparse 3D Point Clouds
TL;DR: A novel cost-based ground measurement model that is incorporated into a Markov Random Field and solved using loopy belief propagation to solve for the maximum belief ground height at each cell is proposed.
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Development of the control system for the Vislab Intercontinental Autonomous Challenge
Alberto Broggi,Paolo Medici,Elena Cardarelli,Pietro Cerri,Alessandro Giacomazzo,Nicola Finardi +5 more
TL;DR: The control system of an autonomous vehicle capable of perceiving and describing the environment using different inputs, such as GPS waypoints, roadways borders and lines, leader vehicles, and obstacles to be avoided, is presented.
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