Road Terrain Classification Technology for Autonomous Vehicle (Record no. 76810)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03140nam a22005775i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-981-13-6155-5 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220801214835.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 190315s2019 si | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9789811361555 |
| -- | 978-981-13-6155-5 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 629.2 |
| 100 1# - AUTHOR NAME | |
| Author | Wang, Shifeng. |
| 245 10 - TITLE STATEMENT | |
| Title | Road Terrain Classification Technology for Autonomous Vehicle |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2019. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XVI, 97 p. 43 illus., 32 illus. in color. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Unmanned System Technologies, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | 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 ## - SUMMARY, ETC. | |
| Summary, etc | 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. . |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-981-13-6155-5 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Singapore : |
| -- | Springer Nature Singapore : |
| -- | Imprint: Springer, |
| -- | 2019. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Automotive engineering. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Transportation engineering. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Traffic engineering. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Signal processing. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Automotive Engineering. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial Intelligence. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Transportation Technology and Traffic Engineering. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Signal, Speech and Image Processing . |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 2523-3742 |
| 912 ## - | |
| -- | ZDB-2-ENG |
| 912 ## - | |
| -- | ZDB-2-SXE |
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