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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Journal ArticleDOI
Study of a Multi-Beam LiDAR Perception Assessment Model for Real-Time Autonomous Driving
Xiaolu Li,Yier Zhou,Baocheng Hua +2 more
TL;DR: In this article, a novel ground segmentation algorithm was proposed with a combination of the grid elevation and the neighbor relationship, which was used to validate how the data quality influences the results of environment perception.
Journal ArticleDOI
Tactical driving decisions of unmanned ground vehicles in complex highway environments: A deep reinforcement learning approach:
TL;DR: The results exhibit the important potentials of the deep Q-network model in learning challenging tactical driving decisions given multiple objectives and complex traffic environment.
Proceedings ArticleDOI
Model-Based Decision Making With Imagination for Autonomous Parking
TL;DR: An imaginative autonomous parking algorithm based on a real kinematic vehicle model is proposed, which makes it more suitable for algorithm application on real autonomous cars and performs better in terms of efficiency and quality.
Book ChapterDOI
Development and Experiences of an Autonomous Vehicle for High-Speed Navigation and Obstacle Avoidance
TL;DR: The autonomous vehicle Pharos, which participated in the 2010 Autonomous Vehicle Competition organized by Hyundai-Kia motors, was developed for high-speed on/off-road unmanned driving avoiding diverse patterns of obstacles.
Integration of Programming and Learning in a Control Language for Autonomous Robots Performing Everyday Activities
TL;DR: In this thesis a robot control language is introduced, which allows to describe declaratively and execute complete learning processes in the program, which includes the identification and recording of experiences, the learning process itself, and the integration of the learning result into the program.
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