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Open AccessJournal ArticleDOI

Stanley: The Robot that Won the DARPA Grand Challenge

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.

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Journal ArticleDOI

Learning from examples in unstructured, outdoor environments

TL;DR: A framework for integrating learning into a standard, hybrid navigation strategy, composed of both plan‐based and reactive controllers is presented, and individual feedback mappings from learned features to learned control actions are introduced as additional behaviors in the behavioral suite.
Journal ArticleDOI

Adaptive finite state machine based visual autonomous navigation system

TL;DR: An original approach applied to autonomous mobile robots navigation integrating localization and navigation using a topological map based on the proposed AFSM (adaptive finite state machine) technique, which is demonstrated to be a promising approach toonomous mobile robots control and navigation.
Journal ArticleDOI

Coastal SLAM With Marine Radar for USV Operation in GPS-Restricted Situations

TL;DR: The relative navigation with respect to the surrounding coastlines is performed in the framework of simultaneous localization and mapping (SLAM) for a USV operation in coastal waters, and coastline features are parameterized by using B-splines for efficient map management, instead of the conventional point cloud representation.
Proceedings ArticleDOI

Sensing requirements for a 13,000 km intercontinental autonomous drive

TL;DR: The design issues that were considered for the equipment of 4 identical autonomous vehicles that will drive themselves without human intervention on an intercontinental route for more than 13,000 km are presented.
Book ChapterDOI

Path Tracking for Automated Driving: A Tutorial on Control System Formulations and Ongoing Research

TL;DR: Different steering-based path tracking algorithms, ranging from geometrical methods to model-predictive controllers, are presented and discussed in this article, together with the expected future research and vehicle implementation directions in the field.
References
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Book

Pattern classification and scene analysis

TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.
Proceedings ArticleDOI

New extension of the Kalman filter to nonlinear systems

TL;DR: It is argued that the ease of implementation and more accurate estimation features of the new filter recommend its use over the EKF in virtually all applications.
Book

Fundamentals of Vehicle Dynamics

TL;DR: In this article, the authors attempt to find a middle ground by balancing engineering principles and equations of use to every automotive engineer with practical explanations of the mechanics involved, so that those without a formal engineering degree can still comprehend and use most of the principles discussed.
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