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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

Learning Image-Conditioned Dynamics Models for Control of Underactuated Legged Millirobots

TL;DR: In this article, the authors presented an approach for controlling a real-world legged millirobot that is based on learned neural network models using less than 17 minutes of data.
Dissertation

An information-driven architecture for cognitive systems research

TL;DR: This thesis bridges the gap between single algorithms and their respective component developers on the one side, and system integration and evaluation on the other by means of a novel integrating approach supporting the collaborative construction of experimental cognitive systems.

Object-level fusion for surround environment perception in automated driving applications

TL;DR: An environment model approach for the detection of dynamic objects is presented in order to realize an effective method for sensor data fusion and has additionally been extensively used in several research projects as the dynamic object detection platform for automated driving applications on highways in real traffic.
Proceedings ArticleDOI

Anytime online novelty detection for vehicle safeguarding

TL;DR: This work presents a novelty detection algorithm that is able to address this sensitivity to high feature dimensionality by utilizing prior class information within the training set and applies it to online detection of novel perception system input on an outdoor mobile robot.
Patent

Analyzing voyage efficiencies

TL;DR: In this paper, the authors present a method, apparatus, and computer program product for analyzing voyage efficiency using historical data for a completed voyage of a ship and a baseline voyage solution.
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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