國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Planning universal on-road driving s...

Heinrich, Steffen.

Planning universal on-road driving strategies for automated vehicles

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Planning universal on-road driving strategies for automated vehiclesby Steffen Heinrich.
作者:	Heinrich, Steffen.
出版者:	Wiesbaden :Springer Fachmedien Wiesbaden :2018.
面頁冊數:	xv, 133 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
標題:	Autonomous vehicles.
電子資源:	http://dx.doi.org/10.1007/978-3-658-21954-3
ISBN:	9783658219543$q(electronic bk.)

Planning universal on-road driving strategies for automated vehicles
Heinrich, Steffen.

Planning universal on-road driving strategies for automated vehicles[electronic resource] /by Steffen Heinrich. - Wiesbaden :Springer Fachmedien Wiesbaden :2018. - xv, 133 p. :ill. (some col.), digital ;24 cm. - AutoUni-schriftenreihe ;Band 119. - AutoUni-schriftenreihe ;Band 119..

A Framework for Universal Driving Strategy Planning -- Sampling-Based Planning in Phase Space -- A Universal Approach for Driving Strategies -- Modeling Ego Motion Uncertainty.

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.

ISBN: 9783658219543$q(electronic bk.)

Standard No.: 10.1007/978-3-658-21954-3doiSubjects--Topical Terms:

736853
Autonomous vehicles.

LC Class. No.: TL152.8 / .H456 2018

Dewey Class. No.: 629.22

Planning universal on-road driving strategies for automated vehicles
LDR:02774nmm a2200337 a 4500 001 537802
003 DE-He213
005 20181112092253.0
006 m d
007 cr nn 008maaau
008 190116s2018 gw s 0 eng d
020 $a 9783658219543$q(electronic bk.)
020 $a 9783658219536$q(paper)
024 7 $a 10.1007/978-3-658-21954-3 $2 doi
035 $a 978-3-658-21954-3
040 $a GP $c GP
041 0 $a eng
050 4 $a TL152.8 $b .H456 2018
072 7 $a TJFM1 $2 bicssc
072 7 $a TEC037000 $2 bisacsh
072 7 $a TEC004000 $2 bisacsh
082 0 4 $a 629.22 $2 23
090 $a TL152.8 $b .H469 2018
100 1 $a Heinrich, Steffen. $3 814916
245 1 0 $a Planning universal on-road driving strategies for automated vehicles $h [electronic resource] / $c by Steffen Heinrich.
260 $a Wiesbaden : $b Springer Fachmedien Wiesbaden : $b Imprint: Springer, $c 2018.
300 $a xv, 133 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a AutoUni-schriftenreihe ; $v Band 119
505 0 $a A Framework for Universal Driving Strategy Planning -- Sampling-Based Planning in Phase Space -- A Universal Approach for Driving Strategies -- Modeling Ego Motion Uncertainty.
520 $a 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.
650 0 $a Autonomous vehicles. $3 736853
650 0 $a Driver assistance systems. $3 515475
650 1 4 $a Engineering. $3 210888
650 2 4 $a Robotics and Automation. $3 357111
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 252959
650 2 4 $a Numeric Computing. $3 275524
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a AutoUni-schriftenreihe ; $v Band 119. $3 814917
856 4 0 $u http://dx.doi.org/10.1007/978-3-658-21954-3
950 $a Engineering (Springer-11647)