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Fundamentals of Vehicle Dynamics

Thomas D. Gillespie1•
University of Michigan1
01 Feb 1992-
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.
Abstract: This book attempts 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. Either as an introductory text or a practical professional overview, this book is an ideal reference.
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Monograph•DOI•
Planning Algorithms: Introductory Material

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Steven M. LaValle
01 Jan 2006
TL;DR: This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms, into planning under differential constraints that arise when automating the motions of virtually any mechanical system.
Abstract: Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

6,340 citations

Journal Article•DOI•
Stanley: The Robot that Won the DARPA Grand Challenge

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Sebastian Thrun1, Michael Montemerlo1, Hendrik Dahlkamp1, David Stavens1, Andrei Aron1, James Diebel1, Philip Fong1, John Gale1, Morgan Halpenny1, Gabriel M. Hoffmann1, Kenny Lau1, Celia M. Oakley1, Mark Palatucci1, Vaughan R. Pratt1, Pascal Stang1, Sven Strohband2, Cedric Dupont2, Lars-Erik Jendrossek2, Christian Koelen2, Charles Markey2, Carlo Rummel2, Joe van Niekerk2, Eric Jensen2, Philippe Alessandrini2, Gary Bradski3, Bob Davies3, Scott M. Ettinger3, Adrian Kaehler3, Ara V. Nefian3, Pamela Mahoney •
Stanford University1, Volkswagen Group of America2, Intel3
01 Sep 2006-Journal of Field Robotics
TL;DR: 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.

2,011 citations

Cites methods from "Fundamentals of Vehicle Dynamics"

  • ...Using a linear bicycle model with infinite tire stiffness and tight steering limitations see Gillespie, 1992 results in the following effect of the control law:...

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Proceedings Article•DOI•
Towards fully autonomous driving: Systems and algorithms

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Jesse Levinson, Jake Askeland1, Jan Becker2, Jennifer Dolson, David Held, Soeren Kammel2, J. Zico Kolter3, Dirk Langer4, Oliver Pink2, Vaughan R. Pratt, Michael Sokolsky, Ganymed Stanek, David Stavens, Alex Teichman, Moritz Werling5, Sebastian Thrun6 •
Volkswagen1, Bosch2, Massachusetts Institute of Technology3, Volkswagen Group of America4, BMW5, Stanford University6
05 Jun 2011
TL;DR: In order to achieve autonomous operation of a vehicle in urban situations with unpredictable traffic, several realtime systems must interoperate, including environment perception, localization, planning, and control.
Abstract: In order to achieve autonomous operation of a vehicle in urban situations with unpredictable traffic, several realtime systems must interoperate, including environment perception, localization, planning, and control. In addition, a robust vehicle platform with appropriate sensors, computational hardware, networking, and software infrastructure is essential.

1,199 citations

Cites methods from "Fundamentals of Vehicle Dynamics"

  • ...To achieve this end, we employ a mixture of a model predictive control (MPC) strategy, based upon wellknown physically based vehicle models [11], along with...

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Journal Article•DOI•
Real-Time Motion Planning With Applications to Autonomous Urban Driving

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Yoshiaki Kuwata1, Sertac Karaman1, Justin Teo1, Emilio Frazzoli1, Jonathan P. How1, Gaston A. Fiore1 •
Massachusetts Institute of Technology1
28 Jul 2009-IEEE Transactions on Control Systems and Technology
TL;DR: The proposed algorithm was at the core of the planning and control software for Team MIT's entry for the 2007 DARPA Urban Challenge, where the vehicle demonstrated the ability to complete a 60 mile simulated military supply mission, while safely interacting with other autonomous and human driven vehicles.
Abstract: This paper describes a real-time motion planning algorithm, based on the rapidly-exploring random tree (RRT) approach, applicable to autonomous vehicles operating in an urban environment. Extensions to the standard RRT are predominantly motivated by: 1) the need to generate dynamically feasible plans in real-time; 2) safety requirements; 3) the constraints dictated by the uncertain operating (urban) environment. The primary novelty is in the use of closed-loop prediction in the framework of RRT. The proposed algorithm was at the core of the planning and control software for Team MIT's entry for the 2007 DARPA Urban Challenge, where the vehicle demonstrated the ability to complete a 60 mile simulated military supply mission, while safely interacting with other autonomous and human driven vehicles.

802 citations

Cites background from "Fundamentals of Vehicle Dynamics"

  • ...The parameter is called the characteristic velocity [33], [34] and can be determined experimentally....

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Journal Article•DOI•
Survey of advanced suspension developments and related optimal control applications

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D. Hrovat1•
Ford Motor Company1
01 Oct 1997-Automatica
TL;DR: While the main emphasis is on Linear-Quadratic optimal control and active suspensions, the paper also addresses a number of related subjects including semi-active suspensions; robust, adaptive and nonlinear control aspects and some of the important practical considerations.

779 citations

Cites background from "Fundamentals of Vehicle Dynamics"

  • ...Bastow (1990),Wong (1993),Gillespie (1994).Earlier advanced suspension surveysfrom the perspectiveof European and Japanese researchers, can be found in Sharp and Crolla (1987)and Nagai (1993),respectively.Aclassifiedbibliographyof more than 500papers related to the design and analysis of advanced ground vehicle suspension systems was compiled by Elbeheiry et al. (1995). It should be mentioned that active suspensions can be integrated with other advanced vehiclesubsystemsinto more effective,overall interactive vehicle control systems....

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  • ...Bastow (1990),Wong (1993),Gillespie (1994).Earlier advanced suspension surveysfrom the perspectiveof European and Japanese researchers, can be found in Sharp and Crolla (1987)and Nagai (1993),respectively....

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J.Y. Wong
Race car vehicle dynamics

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William F. Milliken, Douglas L. Milliken
Vehicle dynamics and control

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