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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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Book ChapterDOI

Autonomous Vehicles: State of the Art, Future Trends, and Challenges

TL;DR: This chapter reports the state of the art, future trends, and challenges of autonomous vehicles, with a special focus on software, using machine learning techniques in order to deal with uncertainties that characterize the environments in which autonomous vehicles will need to operate while guaranteeing safety properties.
Proceedings ArticleDOI

Road detection from aerial imagery

TL;DR: A histogram-based adaptive threshold algorithm is used to detect possible road regions in an image and a probabilistic hough transform based line segment detection combined with a clustering method is implemented to further extract the road.
Journal ArticleDOI

Navigation of an Autonomous Car Using Vector Fields and the Dynamic Window Approach

TL;DR: In this paper, the vector field is associated with a kinematic, feedback linearization controller whose outputs are validated, and eventually modified, by the dynamic window approach for avoiding unmodeled obstacles.
Proceedings ArticleDOI

Robust Sampling Based Model Predictive Control with Sparse Objective Information

TL;DR: An algorithmic framework for stochastic model predictive control that is able to optimize non-linear systems with cost functions that have sparse, discontinuous gradient information is presented.
Proceedings ArticleDOI

Real-Time Signal Light Detection

TL;DR: New algorithm for signal light detection has high detection rate with real time and fast processing at low price and realizes driving with reliability in unmanned ground vehicle.
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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