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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Generating automatic road network definition files for unstructured areas using a multiclass support vector machine
TL;DR: This RNDF, which relies on a Multiclass Support Vector Machine(MSVM)-based trajectory generation method, will be used by an autonomous vehicle for transporting people in closed, unstructured areas for which no previous information is available, such as residential areas or industrial parks.
Crowdsourcing Arctic Navigation Using Multispectral Ice Classification & GNSS
TL;DR: Crowdourcing ice navigation based on a GNSS data registration system offers a framework in which to perform path planning in a reliable and automated way, finding the safest route with the available information and relying less on the expertise of the crew.
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Dynamic Environment Recognition for Autonomous Navigation with Wide FOV 3D-LiDAR
TL;DR: A method to recognize dynamic obstacles from motion of objects without using shape information is proposed and classify point clouds as obstacles from the distance relationship between point to point, and removes the dynamic objects from the point-clouds.
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Pre-Driving Needless System for Autonomous Mobile Robots Navigation in Real World Robot Challenge 2013
Masanobu Saito,Kentaro Kiuchi,Shogo Shimizu,Takayuki Yokota,Yusuke Fujino,Takato Saito,Yoji Kuroda +6 more
Proceedings ArticleDOI
Issues about autonomous cars
Claudiu Pozna,Csaba Antonya +1 more
TL;DR: The paper will start with the mentioned cultural aspects related to a self-driving car and will continue with the big picture of the system.
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