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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

ViNG: Learning Open-World Navigation with Visual Goals

TL;DR: In this article, a learning-based navigation system for reaching visually indicated goals is proposed, which combines a learned policy with a topological graph constructed out of previously observed data, and can determine how to reach this visually indicated goal even in the presence of variable appearance and lighting.
Proceedings ArticleDOI

An Environment to Support Structural Testing of Autonomous Vehicles

TL;DR: A previously proposed testing model and a software tool to support structural testing in the context of autonomous vehicle field testing have been improved to support the generation of new input data from logs collected during field testing using strategies of combination and mutation.
Journal ArticleDOI

Consensus-based local information coordination for the networked control of the autonomous intersection management

TL;DR: This paper introduces the distributed control to a graph-based intersection network to control traffic in a macroscopic level and implements a discrete time consensus algorithm to coordinate the traffic density of an intersection with its neighborhoods and determine the control policy to maximize a traffic throughput of each intersection as well as stabilizing the overall traffic in the network.
Proceedings ArticleDOI

Learning Path Tracking for Real Car-like Mobile Robots From Simulation

TL;DR: Evaluations for running the trained RL network on the real car show that the RL agent can control the car smoothly and reduce the velocity adaptively to follow a sample track.
Proceedings ArticleDOI

Flexible Navigation: Finite state machine-based integrated navigation and control for ROS enabled robots

TL;DR: The Flexible Navigation system is described that extends the ROS Navigation stack and compatible libraries to separate computation from decision making, and integrates the system with FlexBE — the Flexible Behavior Engine, which provides intuitive supervision with adjustable autonomy.
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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