東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Decision-making strategies for autom...

Artunedo, Antonio.

FindBook

Google Book

Amazon

博客來

Decision-making strategies for automated driving in urban environments

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Decision-making strategies for automated driving in urban environments/ by Antonio Artunedo.
作者:	Artunedo, Antonio.
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	xviii, 195 p. :ill., digital ;24 cm.
內容註:	Introduction -- Literature Overview -- Decision Making Architecture -- Global Planning and Mapping -- Motion Prediction and Manoeuvre Planning -- Optimal Trajectory Generation -- Integration and Demonstrations.
Contained By:	Springer eBooks
標題:	Automated vehicles - Decision making. -
電子資源:	https://doi.org/10.1007/978-3-030-45905-5
ISBN:	9783030459055

Decision-making strategies for automated driving in urban environments
Artunedo, Antonio.

Decision-making strategies for automated driving in urban environments[electronic resource] /by Antonio Artunedo. - Cham :Springer International Publishing :2020. - xviii, 195 p. :ill., digital ;24 cm. - Springer theses,2190-5053. - Springer theses..

Introduction -- Literature Overview -- Decision Making Architecture -- Global Planning and Mapping -- Motion Prediction and Manoeuvre Planning -- Optimal Trajectory Generation -- Integration and Demonstrations.

This book describes an effective decision-making and planning architecture for enhancing the navigation capabilities of automated vehicles in the presence of non-detailed, open-source maps. The system involves dynamically obtaining road corridors from map information and utilizing a camera-based lane detection system to update and enhance the navigable space in order to address the issues of intrinsic uncertainty and low-fidelity. An efficient and human-like local planner then determines, within a probabilistic framework, a safe motion trajectory, ensuring the continuity of the path curvature and limiting longitudinal and lateral accelerations. LiDAR-based perception is then used to identify the driving scenario, and subsequently re-plan the trajectory, leading in some cases to adjustment of the high-level route to reach the given destination. The method has been validated through extensive theoretical and experimental analyses, which are reported here in detail.

ISBN: 9783030459055

Standard No.: 10.1007/978-3-030-45905-5doiSubjects--Topical Terms:

3451379
Automated vehicles
--Decision making.

LC Class. No.: TL152.8 / .A788 2020

Dewey Class. No.: 629.046

Decision-making strategies for automated driving in urban environments
LDR:02239nmm a2200337 a 4500 001 2217839
003 DE-He213
005 20200817140558.0
006 m d
007 cr nn 008maaau
008 201120s2020 sz s 0 eng d
020 $a 9783030459055 $q (electronic bk.)
020 $a 9783030459048 $q (paper)
024 7 $a 10.1007/978-3-030-45905-5 $2 doi
035 $a 978-3-030-45905-5
040 $a GP $c GP
041 0 $a eng
050 4 $a TL152.8 $b .A788 2020
072 7 $a TJFM1 $2 bicssc
072 7 $a TEC037000 $2 bisacsh
072 7 $a TJFM1 $2 thema
082 0 4 $a 629.046 $2 23
090 $a TL152.8 $b .A792 2020
100 1 $a Artunedo, Antonio. $3 3451378
245 1 0 $a Decision-making strategies for automated driving in urban environments $h [electronic resource] / $c by Antonio Artunedo.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a xviii, 195 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer theses, $x 2190-5053
505 0 $a Introduction -- Literature Overview -- Decision Making Architecture -- Global Planning and Mapping -- Motion Prediction and Manoeuvre Planning -- Optimal Trajectory Generation -- Integration and Demonstrations.
520 $a This book describes an effective decision-making and planning architecture for enhancing the navigation capabilities of automated vehicles in the presence of non-detailed, open-source maps. The system involves dynamically obtaining road corridors from map information and utilizing a camera-based lane detection system to update and enhance the navigable space in order to address the issues of intrinsic uncertainty and low-fidelity. An efficient and human-like local planner then determines, within a probabilistic framework, a safe motion trajectory, ensuring the continuity of the path curvature and limiting longitudinal and lateral accelerations. LiDAR-based perception is then used to identify the driving scenario, and subsequently re-plan the trajectory, leading in some cases to adjustment of the high-level route to reach the given destination. The method has been validated through extensive theoretical and experimental analyses, which are reported here in detail.
650 0 $a Automated vehicles $x Decision making. $3 3451379
650 0 $a Intelligent transportation systems. $3 1066364
650 1 4 $a Robotics and Automation. $3 1066695
650 2 4 $a Transportation Technology and Traffic Engineering. $3 2153276
650 2 4 $a Operations Research/Decision Theory. $3 890895
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Springer theses. $3 1314442
856 4 0 $u https://doi.org/10.1007/978-3-030-45905-5
950 $a Intelligent Technologies and Robotics (Springer-42732)