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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Autonomous vehicle global navigation approach associating sensor based control and digital maps
TL;DR: A global navigation strategy for autonomous vehicle combining sensor based control and digital maps information to solve the global navigation focusing on two local navigation tasks: road lane following and road intersection maneuvers is proposed.
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A Benchmarking Framework for Control Methods of Maritime Cranes Based on the Functional Mockup Interface
TL;DR: A benchmark framework for advanced control methods of maritime cranes is presented based on the use of the functional mockup interface, allowing the comparison of different control methods independently from the specific crane model to be controlled.
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Autonomous vehicle planning system design under perception limitation in pedestrian environment
Wei Liu,Zhiyong Weng,Zhuangjie Chong,Xiaotong Shen,Scott Pendleton,Baoxing Qin,Guo Ming James Fu,Marcelo H. Ang +7 more
TL;DR: A vehicle planning system for self-driving with limited perception in the pedestrian environment is presented, and only the raw LIDAR sensing data is employed for the purpose of traversability analysis and vehicle planning.
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The Driving School System: Learning Basic Driving Skills From a Teacher in a Real Car
Irene Markelic,Anders Kjær-Nielsen,Karl Pauwels,Nikolay Chumerin,Ausra Vidugiriene,Minija Tamosiunaite,A. Rotter,M.M. Van Hulle,N. Kruger,Florentin Wörgötter +9 more
TL;DR: A system that learns a human's basic driving behavior and demonstrates its use as ADAS by issuing alerts when detecting inconsistent driving behavior, and proposes that this ability to adapt to the driver can lead to better acceptance of ADAS, which is an important sales argument.
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Mind the ground: A power spectral density-based estimator for all-terrain rovers
TL;DR: A method for terrain unevenness estimation that is based on the power spectral density of the surface profile as measured by exteroceptive sensing, that is, by using a common onboard range sensor such as a stereoscopic camera, and validated in the field using an all-terrain rover that operates on various natural surfaces.
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