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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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Posted Content

A Survey of Deep Learning Applications to Autonomous Vehicle Control

TL;DR: In this article, a wide range of research works reported in the literature which aim to control a vehicle through deep learning methods are surveyed, focusing on vehicle control rather than the wider perception problem which includes tasks such as semantic segmentation and object detection.
Journal ArticleDOI

Fast Vanishing-Point Detection in Unstructured Environments

TL;DR: This paper proposes a novel methodology based on image texture analysis for the fast estimation of the vanishing point in challenging and unstructured roads that uses joint activities of only four Gabor filters to precisely estimate the local dominant orientation at each pixel location in the image plane.
Journal IssueDOI

Driving with tentacles: Integral structures for sensing and motion

TL;DR: A LIDAR-based navigation approach applied at both the C-Elrob 2007 and the 2007 DARPA Urban Challenge is described, using a set of “tentacles” that represent precalculated trajectories defined in the ego-centered coordinate space of the vehicle.
Proceedings ArticleDOI

A spotlight on security and privacy risks with future household robots: attacks and lessons

TL;DR: This research experimentally analyze three of today's household robots for security and privacy vulnerabilities and synthesizes the results to construct a set of design questions aimed at facilitating the future development of household robots that are secure and preserve their users' privacy.
Proceedings ArticleDOI

Visual topometric localization

TL;DR: In this paper, a combination of topological and metric mapping is used to encode the coarse topology of the route as well as detailed metric information required for accurate localization, which achieves an average localization error of 2.7 m over this route.
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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