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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Integration of Drive-by-Wire with Navigation Control for a Driverless Electric Race Car
TL;DR: The design and implementation of a drive-by-wire system and a navigation control system for an autonomous Formula SAE race car are presented, resulting in the development of a platform for research into autonomous driving which can be easily replicated.
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Identifying the Operational Design Domain for an Automated Driving System through Assessed Risk
TL;DR: In this article, the authors proposed a methodology to identify an operational design domain (ODD) for an autonomous driving system (ADS) with statistical data and risk tolerance, where the identified ODD is constituted of a geographical map where the risk of ADS operation is lower than the pre-determined risk threshold for a given set of environmental conditions.
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3D computer vision based on machine learning with deep neural networks: A review
TL;DR: This review paper seeks to provide an overview of deep learning in the field of computer vision with an emphasis on recent progress in tasks involving 3D visual data, and through a backdrop of the mammalian visual processing system, hopes to provide inspiration for future advances in automated visual processing.
Proceedings ArticleDOI
GndNet: Fast Ground Plane Estimation and Point Cloud Segmentation for Autonomous Vehicles
TL;DR: A novel end-to-end approach that estimates the ground plane elevation information in a grid-based representation and segments the ground points simultaneously in real-time, establishes a new state-of-the-art, achieves a run-time of 55Hz for ground plane estimation and ground point segmentation.
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Vision-based robust control framework based on deep reinforcement learning applied to autonomous ground vehicles
Gustavo P. Morais,Lucas Barbosa Marcos,Jose Nuno A. D. Bueno,Nilo F. de Resende,Marco H. Terra,Valdir Grassi +5 more
TL;DR: A hybrid control architecture that combines Deep Reinforcement Learning (DRL) and Robust Linear Quadratic Regulator (RLQR) for vision-based lateral control of an autonomous vehicle is presented and significantly decreases the required training time.
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