WEKO3
AND
アイテム
{"_buckets": {"deposit": "bae5840a-1fbe-461b-8ced-61af84e979c1"}, "_deposit": {"id": "7875", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "7875"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00007875"}, "item_10_biblio_info_6": {"attribute_name": "\u66f8\u8a8c\u60c5\u5831", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2007", "bibliographicIssueDateType": "Issued"}, "bibliographicPageEnd": "827", "bibliographicPageStart": "823", "bibliographic_titles": [{"bibliographic_title": "IEEE Intelligent Vehicles Symposium"}]}]}, "item_10_description_4": {"attribute_name": "\u6284\u9332", "attribute_value_mlt": [{"subitem_description": "This paper presents a method to generate car-following patterns for individual drivers. We assume that driving is a recursive process. A driver recognizes a road environment such as velocity and following distance and adjusts gas and brake pedal positions. A vehicle status changes according to the driver\u0027s operation and the road environment changes according to the vehicle status. Driving patterns of each driver are modeled with a Gaussian mixture model (GMM), which is trained as a joint probability distribution of following distance, velocity, pedal position signals and their dynamics. Gas and brake pedal operation patterns are generated from the GMMs in a maximum likelihood criterion so that the conditional probability is maximized for a given environment i.e., following distance and velocity. Experimental results for a driving simulator show that car-following patterns generated from GMMs for three different drivers maintain their individual driving characteristics.", "subitem_description_type": "Abstract"}]}, "item_10_identifier_60": {"attribute_name": "URI", "attribute_value_mlt": [{"subitem_identifier_type": "HDL", "subitem_identifier_uri": "http://hdl.handle.net/2237/9597"}]}, "item_10_publisher_32": {"attribute_name": "\u51fa\u7248\u8005", "attribute_value_mlt": [{"subitem_publisher": "IEEE"}]}, "item_10_relation_11": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_type_id": {"subitem_relation_type_id_text": "http://dx.doi.org/10.1109/IVS.2007.4290218", "subitem_relation_type_select": "DOI"}}]}, "item_10_relation_8": {"attribute_name": "ISBN", "attribute_value_mlt": [{"subitem_relation_type_id": {"subitem_relation_type_id_text": "1-4244-1068-1", "subitem_relation_type_select": "ISBN"}}]}, "item_10_rights_12": {"attribute_name": "\u6a29\u5229", "attribute_value_mlt": [{"subitem_rights": "Copyright \u00a9 2007 IEEE. Reprinted from (relevant publication info). This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Nagoya University\u2019s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org."}]}, "item_10_select_15": {"attribute_name": "\u8457\u8005\u7248\u30d5\u30e9\u30b0", "attribute_value_mlt": [{"subitem_select_item": "publisher"}]}, "item_10_source_id_7": {"attribute_name": "ISSN", "attribute_value_mlt": [{"subitem_source_identifier": "1931-0587", "subitem_source_identifier_type": "ISSN"}]}, "item_10_text_14": {"attribute_name": "\u30d5\u30a9\u30fc\u30de\u30c3\u30c8", "attribute_value_mlt": [{"subitem_text_value": "application/pdf"}]}, "item_creator": {"attribute_name": "\u8457\u8005", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Nishiwaki, Yoshihiro"}], "nameIdentifiers": [{"nameIdentifier": "22512", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Miyajima, Chiyomi"}], "nameIdentifiers": [{"nameIdentifier": "22513", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Kitaoka, Norihide"}], "nameIdentifiers": [{"nameIdentifier": "22514", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Itou, Katsunobu"}], "nameIdentifiers": [{"nameIdentifier": "22515", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Takeda, Kazuya"}], "nameIdentifiers": [{"nameIdentifier": "22516", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "\u30d5\u30a1\u30a4\u30eb\u60c5\u5831", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2018-02-19"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "Miyajima_2.pdf", "filesize": [{"value": "562.5 kB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 562500.0, "url": {"label": "Miyajima_2.pdf", "url": "https://nagoya.repo.nii.ac.jp/record/7875/files/Miyajima_2.pdf"}, "version_id": "9fd60f29-1541-4652-9490-1f9caf5745f6"}]}, "item_language": {"attribute_name": "\u8a00\u8a9e", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "\u8cc7\u6e90\u30bf\u30a4\u30d7", "attribute_value_mlt": [{"resourcetype": "journal article", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "Generation of Pedal Operation Patterns of Individual Drivers in Car-Following for Personalized Cruise Control", "item_titles": {"attribute_name": "\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_title": "Generation of Pedal Operation Patterns of Individual Drivers in Car-Following for Personalized Cruise Control"}]}, "item_type_id": "10", "owner": "1", "path": ["312/313/314"], "permalink_uri": "http://hdl.handle.net/2237/9597", "pubdate": {"attribute_name": "\u516c\u958b\u65e5", "attribute_value": "2008-03-18"}, "publish_date": "2008-03-18", "publish_status": "0", "recid": "7875", "relation": {}, "relation_version_is_last": true, "title": ["Generation of Pedal Operation Patterns of Individual Drivers in Car-Following for Personalized Cruise Control"], "weko_shared_id": 3}
Generation of Pedal Operation Patterns of Individual Drivers in Car-Following for Personalized Cruise Control
http://hdl.handle.net/2237/9597
6e398174-a410-426f-a751-d70cd136628d
名前 / ファイル | ライセンス | アクション | |
---|---|---|---|
![]() |
|
Item type | 学術雑誌論文 / Journal Article(1) | |||||
---|---|---|---|---|---|---|
公開日 | 2008-03-18 | |||||
タイトル | ||||||
タイトル | Generation of Pedal Operation Patterns of Individual Drivers in Car-Following for Personalized Cruise Control | |||||
著者 |
Nishiwaki, Yoshihiro
× Nishiwaki, Yoshihiro× Miyajima, Chiyomi× Kitaoka, Norihide× Itou, Katsunobu× Takeda, Kazuya |
|||||
権利 | ||||||
権利情報 | Copyright © 2007 IEEE. Reprinted from (relevant publication info). This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Nagoya University’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. | |||||
抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | This paper presents a method to generate car-following patterns for individual drivers. We assume that driving is a recursive process. A driver recognizes a road environment such as velocity and following distance and adjusts gas and brake pedal positions. A vehicle status changes according to the driver's operation and the road environment changes according to the vehicle status. Driving patterns of each driver are modeled with a Gaussian mixture model (GMM), which is trained as a joint probability distribution of following distance, velocity, pedal position signals and their dynamics. Gas and brake pedal operation patterns are generated from the GMMs in a maximum likelihood criterion so that the conditional probability is maximized for a given environment i.e., following distance and velocity. Experimental results for a driving simulator show that car-following patterns generated from GMMs for three different drivers maintain their individual driving characteristics. | |||||
出版者 | ||||||
出版者 | IEEE | |||||
言語 | ||||||
言語 | eng | |||||
資源タイプ | ||||||
資源 | http://purl.org/coar/resource_type/c_6501 | |||||
タイプ | journal article | |||||
DOI | ||||||
関連識別子 | ||||||
識別子タイプ | DOI | |||||
関連識別子 | http://dx.doi.org/10.1109/IVS.2007.4290218 | |||||
ISBN | ||||||
関連識別子 | ||||||
識別子タイプ | ISBN | |||||
関連識別子 | 1-4244-1068-1 | |||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 1931-0587 | |||||
書誌情報 |
IEEE Intelligent Vehicles Symposium p. 823-827, 発行日 2007 |
|||||
フォーマット | ||||||
application/pdf | ||||||
著者版フラグ | ||||||
値 | publisher | |||||
URI | ||||||
識別子 | http://hdl.handle.net/2237/9597 | |||||
識別子タイプ | HDL |