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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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Citations
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Journal ArticleDOI

Real‐time vehicle detection and tracking using 3D LiDAR

TL;DR: In order to increase the accuracy of vehicle detection and tracking, a new clustering algorithm is proposed to obtain vehicle candidates from preprocessed point cloud data collected by the LiDAR.
Journal ArticleDOI

Vision-Based Steering Control, Speed Assistance and Localization for Inner-City Vehicles

TL;DR: Real driving tests with a commercial car on a closed circuit finally prove the applicability of the derived approach, which proposes the use of a single monocular camera sensor for an automatic steering control, speed assistance for the driver and localization of the vehicle on a road.
Proceedings ArticleDOI

Real-time dynamic power management based on Pearson's Correlation Coefficient

TL;DR: A new environment observer method based on Pearson's Correlation Coefficient is proposed, which permits that some logical components may be shut down to save processor energy consumption, and/or to make the CPU available for running concurrent processes.
Journal ArticleDOI

Motion Modeling and Localization of Skid-Steering Wheeled Rover on Loose Terrain

TL;DR: A mathematical model is derived that expresses a relationship between the input and output velocities of the rover's wheels and the effectiveness of a position and motion estimation method based on the model was confirmed from experiments conducted in the sand 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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