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

A reliability-aware fusion concept t...

Nguyen, Tuan Tran.

A reliability-aware fusion concept toward robust ego-lane estimation incorporating multiple sources

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A reliability-aware fusion concept toward robust ego-lane estimation incorporating multiple sourcesby Tuan Tran Nguyen.
作者:	Nguyen, Tuan Tran.
出版者:	Wiesbaden :Springer Fachmedien Wiesbaden :2020.
面頁冊數:	xxiii, 164 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Context-aware computing.
電子資源:	https://doi.org/10.1007/978-3-658-26949-4
ISBN:	9783658269494$q(electronic bk.)

A reliability-aware fusion concept toward robust ego-lane estimation incorporating multiple sources
Nguyen, Tuan Tran.

A reliability-aware fusion concept toward robust ego-lane estimation incorporating multiple sources[electronic resource] /by Tuan Tran Nguyen. - Wiesbaden :Springer Fachmedien Wiesbaden :2020. - xxiii, 164 p. :ill., digital ;24 cm. - AutoUni - schriftenreihe,band 1401867-3635 ;. - AutoUni - schriftenreihe ;Band 94..

Reliability-Aware Fusion Framework -- Assessing and Learning Reliability for Ego-Lane Estimation -- Reliability-Based Ego-Lane Estimation Using Multiple Sources.

To tackle the challenges of the road estimation task, many works employ a fusion of multiple sources. By that, a commonly made assumption is that the sources always are equally reliable. However, this assumption is inappropriate since each source has certain advantages and drawbacks depending on the operational scenarios. Therefore, Tuan Tran Nguyen proposes a novel concept by incorporating reliabilities into the multi-source fusion so that the road estimation task can alternately select only the most reliable sources. Thereby, the author estimates the reliability for each source online using classifiers trained with the sensor measurements, the past performance and the context. Using real data recordings, he shows via experimental results that the presented reliability-aware fusion increases the availability of automated driving up to 7 percentage points compared to the average fusion. Contents Reliability-Aware Fusion Framework Assessing and Learning Reliability for Ego-Lane Estimation Reliability-Based Ego-Lane Estimation Using Multiple Sources Target Groups Scientists and students in the fields of IT, fusion and automated driving Engineers working in industrial research and development of automated driving About the Author Tuan Tran Nguyen received the Master's degree in computer science and the Ph.D. degree from Otto-von-Guericke University Magdeburg, Germany, in 2013 and 2019, respectively. His research focuses on methods and architectures for reliability-based sensor fusion in intelligent vehicles.

ISBN: 9783658269494$q(electronic bk.)

Standard No.: 10.1007/978-3-658-26949-4doiSubjects--Topical Terms:

378559
Context-aware computing.

LC Class. No.: QA76.5915

Dewey Class. No.: 004

A reliability-aware fusion concept toward robust ego-lane estimation incorporating multiple sources
LDR:02782nmm a2200337 a 4500 001 578172
003 DE-He213
005 20200213170801.0
006 m d
007 cr nn 008maaau
008 201208s2020 gw s 0 eng d
020 $a 9783658269494$q(electronic bk.)
020 $a 9783658269487$q(paper)
024 7 $a 10.1007/978-3-658-26949-4 $2 doi
035 $a 978-3-658-26949-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.5915
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 004 $2 23
090 $a QA76.5915 $b .N576 2020
100 1 $a Nguyen, Tuan Tran. $3 866762
245 1 2 $a A reliability-aware fusion concept toward robust ego-lane estimation incorporating multiple sources $h [electronic resource] / $c by Tuan Tran Nguyen.
260 $a Wiesbaden : $b Springer Fachmedien Wiesbaden : $b Imprint: Springer, $c 2020.
300 $a xxiii, 164 p. : $b ill., digital ; $c 24 cm.
490 1 $a AutoUni - schriftenreihe, $x 1867-3635 ; $v band 140
505 0 $a Reliability-Aware Fusion Framework -- Assessing and Learning Reliability for Ego-Lane Estimation -- Reliability-Based Ego-Lane Estimation Using Multiple Sources.
520 $a To tackle the challenges of the road estimation task, many works employ a fusion of multiple sources. By that, a commonly made assumption is that the sources always are equally reliable. However, this assumption is inappropriate since each source has certain advantages and drawbacks depending on the operational scenarios. Therefore, Tuan Tran Nguyen proposes a novel concept by incorporating reliabilities into the multi-source fusion so that the road estimation task can alternately select only the most reliable sources. Thereby, the author estimates the reliability for each source online using classifiers trained with the sensor measurements, the past performance and the context. Using real data recordings, he shows via experimental results that the presented reliability-aware fusion increases the availability of automated driving up to 7 percentage points compared to the average fusion. Contents Reliability-Aware Fusion Framework Assessing and Learning Reliability for Ego-Lane Estimation Reliability-Based Ego-Lane Estimation Using Multiple Sources Target Groups Scientists and students in the fields of IT, fusion and automated driving Engineers working in industrial research and development of automated driving About the Author Tuan Tran Nguyen received the Master's degree in computer science and the Ph.D. degree from Otto-von-Guericke University Magdeburg, Germany, in 2013 and 2019, respectively. His research focuses on methods and architectures for reliability-based sensor fusion in intelligent vehicles.
650 0 $a Context-aware computing. $3 378559
650 0 $a Wireless sensor networks. $3 376151
650 1 4 $a Computational Intelligence. $3 338479
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
650 2 4 $a Robotics. $3 181952
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a AutoUni - schriftenreihe ; $v Band 94. $3 770553
856 4 0 $u https://doi.org/10.1007/978-3-658-26949-4
950 $a Intelligent Technologies and Robotics (Springer-42732)