Planning Universal On-Road Driving Strategies for Automated Vehicles (Record no. 77894)

000 -LEADER
fixed length control field 03725nam a22005655i 4500
001 - CONTROL NUMBER
control field 978-3-658-21954-3
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220801215822.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 180419s2018 gw | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783658219543
-- 978-3-658-21954-3
082 04 - CLASSIFICATION NUMBER
Call Number 629.8
100 1# - AUTHOR NAME
Author Heinrich, Steffen.
245 10 - TITLE STATEMENT
Title Planning Universal On-Road Driving Strategies for Automated Vehicles
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2018.
300 ## - PHYSICAL DESCRIPTION
Number of Pages XV, 133 p. 59 illus., 25 illus. in color.
490 1# - SERIES STATEMENT
Series statement AutoUni – Schriftenreihe,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 A Framework for Universal Driving Strategy Planning -- Sampling-Based Planning in Phase Space -- A Universal Approach for Driving Strategies -- Modeling Ego Motion Uncertainty.
520 ## - SUMMARY, ETC.
Summary, etc Steffen Heinrich describes a motion planning system for automated vehicles. The planning method is universally applicable to on-road scenarios and does not depend on a high-level maneuver selection automation for driving strategy guidance. The author presents a planning framework using graphics processing units (GPUs) for task parallelization. A method is introduced that solely uses a small set of rules and heuristics to generate driving strategies. It was possible to show that GPUs serve as an excellent enabler for real-time applications of trajectory planning methods. Like humans, computer-controlled vehicles have to be fully aware of their surroundings. Therefore, a contribution that maximizes scene knowledge through smart vehicle positioning is evaluated. A post-processing method for stochastic trajectory validation supports the search for longer-term trajectories which take ego-motion uncertainty into account. Contents A Framework for Universal Driving Strategy Planning Sampling-Based Planning in Phase Space A Universal Approach for Driving Strategies Modeling Ego Motion Uncertainty Target Groups Scientists and students in the field of robotics, computer science, mechanical engineering Engineers in the field of vehicle automation, intelligent systems and robotics About the Author Steffen Heinrich has a strong background in robotics and artificial intelligence. Since 2009 he has been developing algorithms and software components for self-driving systems in research facilities and for automakers in Germany and the US.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-658-21954-3
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Wiesbaden :
-- Springer Fachmedien Wiesbaden :
-- Imprint: Springer,
-- 2018.
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-- text
-- txt
-- rdacontent
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-- computer
-- c
-- rdamedia
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-- online resource
-- cr
-- rdacarrier
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-- text file
-- PDF
-- rda
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Control engineering.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Robotics.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Automation.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial intelligence.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Numerical analysis.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Control, Robotics, Automation.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial Intelligence.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Numerical Analysis.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 2512-1154 ;
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-- ZDB-2-ENG
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-- ZDB-2-SXE

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