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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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Proceedings ArticleDOI

Skill-based anytime agent architecture for logistics and manipulation tasks: EuRoC Challenge 2, Stage II - Realistic Labs: Benchmarking

TL;DR: The TIMAIRIS architecture and approach used in the Challenge 2 - Stage II - Benchmarking phase is presented, namely regarding the perception, manipulation and planning strategy that was applied to achieve the tasks objectives and proved to be quite robust and efficient, which allowed it to rank first in the Benchmarked phase.
Book ChapterDOI

Model Based Vehicle Localization via an Iterative Parameter Estimation

TL;DR: A novel method for the estimation of the wheel circumferences, which have significant effects on a vehicle model based localization, which has a significant impact on the dynamics of the vehicle and the further estimations is proposed.
Journal ArticleDOI

An incremental nonparametric Bayesian clustering-based traversable region detection method

TL;DR: This study presents a self-supervised on-line learning architecture to estimate the traversability in complex and unknown outdoor environments by clustering appearance data using the newly proposed incremental nonparametric Bayesian clustering algorithm.
Journal ArticleDOI

Empirical Evaluation of an Autonomous Vehicle in an Urban Environment

TL;DR: A novel autonomous platform developed by the Sydney-Berkeley Driving Team for entry into the 2007 DARPA Urban Challenge competition is described and it is shown that the system is robust against failure of global position estimates and can reliably traverse standard two-lane road networks using vision for localization.
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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