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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Off-road Robotics—An Overview
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Comparative psychology and the grand challenge of drug discovery in psychiatry and neurodegeneration
TL;DR: It is argued that many of the difficulties facing CNS drug discovery stem from a lack of robustness in pre-clinical testing, and these translational difficulties are presented as a "grand challenge" to researchers from comparative cognition, who are well positioned to provide new methods for testing behavior and cognition in non-human animals.
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The path to more general artificial intelligence
TL;DR: A small-N comparative analysis of six different areas of applied artificial intelligence suggests that the next period of development will require a merging of narrow-AI and strong-AI approaches, necessary as programmers seek to move beyond developing narrowly defined tools to developing software agents capable of acting independently in complex environments.
Proceedings ArticleDOI
Fast Iterative Closest Point framework for 3D LIDAR data in intelligent vehicle
TL;DR: This paper presents a remarkably efficient search procedure, exploiting two concepts of approximate nearest neighbor and local search, which is about 24 times faster than the standard k-d tree.
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A Laser-Scanner-Based Approach Toward Driving Safety and Traffic Data Collection
TL;DR: This work is motivated by the following two potential applications: enhancing driving safety and collecting traffic data in a large dynamic urban environment, and a laser-scanner-based approach is proposed, which is formulated as a simultaneous localization and mapping with object tracking and classification.
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