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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Knowing when we don't know: Introspective classification for mission-critical decision making
TL;DR: This paper introduces and motivate the importance of a classifier's introspective capacity: the ability to mitigate potentially overconfident classifications by an appropriate assessment of how qualified the system is to make a judgement on the current test datum.
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Comparison of lateral controllers for autonomous vehicle: Experimental results
TL;DR: Three classical techniques used for controlling the lateral error are analyzed and a novel kinematic controller based on the lateral speed is proposed, which will help to improve the lateral control of a self-driving car in an urban environment.
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An Autonomous Driving System for Unknown Environments Using a Unified Map
Inwook Shim,Jongwon Choi,Seunghak Shin,Tae-Hyun Oh,Unghui Lee,Byung Tae Ahn,Dong-Geol Choi,David Hyunchul Shim,In So Kweon +8 more
TL;DR: This work proposes algorithms and systems using Unified Map built with various onboard sensors to detect obstacles, other cars, traffic signs, and pedestrians, and shows how this map can efficiently find paths free from collisions while obeying traffic laws.
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Extrinsic Calibration of 2-D Lidars Using Two Orthogonal Planes
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