| Item type |
学術雑誌論文 / Journal Article.(1) |
| 公開日 |
2019-07-16 |
| 書誌情報 |
en : IEEE Transactions on Vehicular Technology
巻 67,
号 8,
p. 6814-6823,
発行日 2018-04-03
|
| タイトル |
|
|
タイトル |
Human-Like Driving: Empirical Decision-Making System for Autonomous Vehicles |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Self-driving |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
autonomous vehicles |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
collision avoidance system |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
vehicle control |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
machine learning |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 著者 |
李, 良知
太田, 香
董, 冕雄
|
| 室蘭工業大学研究者データベースへのリンク |
|
|
表示名 |
太田 香（OTA Kaoru） |
|
URL |
http://rdsoran.muroran-it.ac.jp/html/100000140_ja.html |
| 室蘭工業大学研究者データベースへのリンク |
|
|
表示名 |
董 冕雄（DONG Mianxiong） |
|
URL |
http://rdsoran.muroran-it.ac.jp/html/100000145_ja.html |
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
The autonomous vehicle, as an emerging and rapidly growing field, has received extensive attention for its futuristic driving experiences. Although the fast developing depth sensors and machine learning methods have given a huge boost to self-driving research, existing autonomous driving vehicles do meet with several avoidable accidents during their road testings. The major cause is the misunderstanding between self-driving systems and human drivers. To solve this problem, we propose a humanlike driving system in this paper to give autonomous vehicles the ability to make decisions like a human. In our method, a convolutional neural network model is used to detect, recognize, and abstract the information in the input road scene, which is captured by the on-board sensors. And then a decision-making system calculates the specific commands to control the vehicles based on the abstractions. The biggest advantage of our work is that we implement a decision-making system which can well adapt to real-life road conditions, in which a massive number of human drivers exist. In addition, we build our perception system with only the depth information, rather than the unstable RGB data. The experimental results give a good demonstration of the efficiency and robustness of the proposed method. |
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言語 |
en |
| 出版者 |
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出版者 |
IEEE |
|
言語 |
en |
| 出版者版へのリンク |
|
|
表示名 |
10.1109/TVT.2018.2822762 |
|
URL |
https://doi.org/10.1109/TVT.2018.2822762 |
| DOI |
|
|
関連タイプ |
isVersionOf |
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
10.1109/TVT.2018.2822762 |
| 日本十進分類法 |
|
|
主題Scheme |
NDC |
|
主題 |
007 |
| ISSN |
|
|
収録物識別子タイプ |
PISSN |
|
収録物識別子 |
0018-9545 |
| 書誌レコードID |
|
|
収録物識別子タイプ |
NCID |
|
収録物識別子 |
AA00668186 |
| 権利 |
|
|
言語 |
en |
|
権利情報 |
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| 著者版フラグ |
|
|
出版タイプ |
AM |
|
出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
| フォーマット |
|
|
内容記述タイプ |
Other |
|
内容記述 |
application/pdf |
IEEETVT_67_8_6814_6823 (3.3 MB)
