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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

Computer Vision: Algorithms and Applications

TL;DR: Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images and takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene.
Journal ArticleDOI

A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles

TL;DR: In this article, the authors present a survey of the state of the art on planning and control algorithms with particular regard to the urban environment, along with a discussion of their effectiveness.
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Predictive Active Steering Control for Autonomous Vehicle Systems

TL;DR: The effectiveness of the proposed MPC formulation is demonstrated by simulation and experimental tests up to 21 m/s on icy roads, and two approaches with different computational complexities are presented.
References
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Proceedings ArticleDOI

Self-supervised Monocular Road Detection in Desert Terrain

TL;DR: This method for identifying drivable surfaces in difficult unpaved and offroad terrain conditions as encountered in the DARPA Grand Challenge robot race achieves robustness by combining sensor information from a laser range finder, a pose estimation system and a color camera.
Proceedings ArticleDOI

A task description language for robot control

TL;DR: TDL is an extension of C++ that provides syntactic support for task decomposition, synchronization, execution monitoring, and exception handling, and a compiler transforms TDL into pure C++ code that utilizes a platform-independent task management library.
Journal ArticleDOI

Rapidly adapting machine vision for automated vehicle steering

TL;DR: The Ralph vision system helps automobile drivers steer, by sampling an image, assessing the road curvature, and determining the lateral offset of the vehicle relative to the lane center.
Proceedings ArticleDOI

Vision-guided flight stability and control for micro air vehicles

TL;DR: This paper motivate the use of computer vision for MAV autonomy, arguing that given current sensor technology, vision may be the only practical approach to the problem, and describes the statistical vision-based horizon detection algorithm, which has been demonstrated at 30 Hz with over 99.9% correct horizon identification.
Journal ArticleDOI

SCARF: a color vision system that tracks roads and intersections

TL;DR: The SCARF system is described in detail, results on a variety of images are presented, and Navlab test runs usingSCARF are discussed.
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