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

Road terrain classification technolo...

Wang, Shifeng.

FindBook

Google Book

Amazon

博客來

Road terrain classification technology for autonomous vehicle

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Road terrain classification technology for autonomous vehicle/ by Shifeng Wang.
作者:	Wang, Shifeng.
出版者:	Singapore :Springer Singapore : : 2019.,
面頁冊數:	xvi, 97 p. :ill., digital ;24 cm.
內容註:	Introduction -- Review of Related Work -- Acceleration Based Road Terrain Classification -- Image Based Road Terrain Classification -- LRF Based Road Terrain Classification -- Multiple-Sensor Based Road Terrain Classification -- Conclusion and Future Direction.
Contained By:	Springer eBooks
標題:	Autonomous vehicles. -
電子資源:	https://doi.org/10.1007/978-981-13-6155-5
ISBN:	9789811361555

Road terrain classification technology for autonomous vehicle
Wang, Shifeng.

Road terrain classification technology for autonomous vehicle[electronic resource] /by Shifeng Wang. - Singapore :Springer Singapore :2019. - xvi, 97 p. :ill., digital ;24 cm. - Unmanned system technologies,2523-3734. - Unmanned system technologies..

Introduction -- Review of Related Work -- Acceleration Based Road Terrain Classification -- Image Based Road Terrain Classification -- LRF Based Road Terrain Classification -- Multiple-Sensor Based Road Terrain Classification -- Conclusion and Future Direction.

This book provides cutting-edge insights into autonomous vehicles and road terrain classification, and introduces a more rational and practical method for identifying road terrain. It presents the MRF algorithm, which combines the various sensors' classification results to improve the forward LRF for predicting upcoming road terrain types. The comparison between the predicting LRF and its corresponding MRF show that the MRF multiple-sensor fusion method is extremely robust and effective in terms of classifying road terrain. The book also demonstrates numerous applications of road terrain classification for various environments and types of autonomous vehicle, and includes abundant illustrations and models to make the comparison tables and figures more accessible.

ISBN: 9789811361555

Standard No.: 10.1007/978-981-13-6155-5doiSubjects--Topical Terms:

2179092
Autonomous vehicles.

LC Class. No.: TL152.8

Dewey Class. No.: 629.046

Road terrain classification technology for autonomous vehicle
LDR:02074nmm a2200349 a 4500 001 2180513
003 DE-He213
005 20190315192041.0
006 m d
007 cr nn 008maaau
008 191122s2019 si s 0 eng d
020 $a 9789811361555 $q (electronic bk.)
020 $a 9789811361548 $q (paper)
024 7 $a 10.1007/978-981-13-6155-5 $2 doi
035 $a 978-981-13-6155-5
040 $a GP $c GP
041 0 $a eng
050 4 $a TL152.8
072 7 $a TRC $2 bicssc
072 7 $a TEC009090 $2 bisacsh
072 7 $a TRC $2 thema
072 7 $a TRCS $2 thema
082 0 4 $a 629.046 $2 23
090 $a TL152.8 $b .W246 2019
100 1 $a Wang, Shifeng. $3 3386648
245 1 0 $a Road terrain classification technology for autonomous vehicle $h [electronic resource] / $c by Shifeng Wang.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2019.
300 $a xvi, 97 p. : $b ill., digital ; $c 24 cm.
490 1 $a Unmanned system technologies, $x 2523-3734
505 0 $a Introduction -- Review of Related Work -- Acceleration Based Road Terrain Classification -- Image Based Road Terrain Classification -- LRF Based Road Terrain Classification -- Multiple-Sensor Based Road Terrain Classification -- Conclusion and Future Direction.
520 $a This book provides cutting-edge insights into autonomous vehicles and road terrain classification, and introduces a more rational and practical method for identifying road terrain. It presents the MRF algorithm, which combines the various sensors' classification results to improve the forward LRF for predicting upcoming road terrain types. The comparison between the predicting LRF and its corresponding MRF show that the MRF multiple-sensor fusion method is extremely robust and effective in terms of classifying road terrain. The book also demonstrates numerous applications of road terrain classification for various environments and types of autonomous vehicle, and includes abundant illustrations and models to make the comparison tables and figures more accessible.
650 0 $a Autonomous vehicles. $3 2179092
650 1 4 $a Automotive Engineering. $3 928032
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Transportation Technology and Traffic Engineering. $3 2153276
650 2 4 $a Signal, Image and Speech Processing. $3 891073
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Unmanned system technologies. $3 3381537
856 4 0 $u https://doi.org/10.1007/978-981-13-6155-5
950 $a Engineering (Springer-11647)