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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

Perception and control strategies for driving utility vehicles with a humanoid robot

TL;DR: Low- and higher-level methods are presented for speed control, environment perception, and trajectory planning and following suitable for operation in planar areas with discrete obstacles as well as along road-like paths.
Proceedings Article

Local Trajectory Planning and Tracking For Autonomous Vehicle Navigation Using Clothoid Tentacles Method

TL;DR: The planning method developed in this work uses an empirical approach for local path planning on drawing clothoid tentacles in the egocentered reference frame related to the vehicle based on Immersion and Invariance principle.
Journal ArticleDOI

Towards behaviour based testing to understand the black box of autonomous cars

TL;DR: Four approaches are highlighted that might help understanding black box technical systems for autonomous cars by focusing on its behaviour by using the method of experimental psychology to model the inner workings of DNNs by observing its behaviour in specific situations.
Journal ArticleDOI

An Efficient and Scalable Simulation Model for Autonomous Vehicles With Economical Hardware

TL;DR: Results indicate that this model can achieve real-time response on a resource-constrained device without significant overheads, thus making it a suitable candidate for autonomous driving in current intelligent transportation systems.

Evaluation of precise point positioning using MADOCA-LEX via Quasi-Zenith satellite system

TL;DR: A novel technique for receiving and decoding the MADOCA-LEX message using a software GNSS receiver is developed and it is found that the proposed localization technique is effective for position estimation with the decimeterlevel accuracy through a kinematic test in the open-sky environment.
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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