{"_buckets": {"deposit": "51c88bef-5475-4a0d-9fdf-aa7a0b7e399a"}, "_deposit": {"id": "8705", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "8705"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00008705"}, "item_18_biblio_info_6": {"attribute_name": "\u66f8\u8a8c\u60c5\u5831", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2005-12", "bibliographicIssueDateType": "Issued"}, "bibliographicPageEnd": "48", "bibliographicPageStart": "45", "bibliographic_titles": [{"bibliographic_title": "Third Symposium on \"Intelligent Media Integration for Social Information Infrastructure\" December 6, 2005"}]}]}, "item_18_description_4": {"attribute_name": "\u6284\u9332", "attribute_value_mlt": [{"subitem_description": "Driving behavior modeling using such driving signals as velocity, following distance, and gas or brake pedal operations, has been investigated for accident prevention and vehicle design. Driving behaviors are different among drivers, and research on driver modeling has also been carried out from different points of view in cognitive and engineering approaches. In this paper, driver\u0027s characteristics in driving behaviors are modeled with a Gaussian mixture model(GMM) using \"cepstral features\" obtained through spectral analysis of gas pedal operation signals. The GMM driver model based on cepstral features in evaluated in driver identification experiments and compared with a conventional GMM driver model that uses raw driving signals without spectral analysis. Experimental results show that the proposed driver model achieves an 89.6% driver identification rate, resulting in 61% error reduction over the conventional driver model.", "subitem_description_type": "Abstract"}]}, "item_18_identifier_60": {"attribute_name": "URI", "attribute_value_mlt": [{"subitem_identifier_type": "HDL", "subitem_identifier_uri": "http://hdl.handle.net/2237/10456"}]}, "item_18_publisher_32": {"attribute_name": "\u51fa\u7248\u8005", "attribute_value_mlt": [{"subitem_publisher": "INTELLIGENT MEDIA INTEGRATION  NAGOYA UNIVERSITY / COE"}]}, "item_18_select_15": {"attribute_name": "\u8457\u8005\u7248\u30d5\u30e9\u30b0", "attribute_value_mlt": [{"subitem_select_item": "publisher"}]}, "item_18_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": "Ozawa, Koji"}], "nameIdentifiers": [{"nameIdentifier": "24478", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Wakita, Toshihiro"}], "nameIdentifiers": [{"nameIdentifier": "24479", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Miyajima, Chiyomi"}], "nameIdentifiers": [{"nameIdentifier": "24480", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Itou, Katsunobu"}], "nameIdentifiers": [{"nameIdentifier": "24481", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Takeda, Kazuya"}], "nameIdentifiers": [{"nameIdentifier": "24482", "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": "p45-48_Modeling_of_Individualities.pdf", "filesize": [{"value": "15.4 MB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 15400000.0, "url": {"label": "p45-48_Modeling_of_Individualities.pdf", "url": "https://nagoya.repo.nii.ac.jp/record/8705/files/p45-48_Modeling_of_Individualities.pdf"}, "version_id": "021a86a9-5a67-40d6-a218-c316c17a762c"}]}, "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": "conference paper", "resourceuri": "http://purl.org/coar/resource_type/c_5794"}]}, "item_title": "MODELING OF INDIVIDUALITIES IN DRIVING THROUGH SPECTRAL ANALYSIS OF BEHAVIORAL SIGNALS", "item_titles": {"attribute_name": "\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_title": "MODELING OF INDIVIDUALITIES IN DRIVING THROUGH SPECTRAL ANALYSIS OF BEHAVIORAL SIGNALS", "subitem_title_language": "en"}]}, "item_type_id": "18", "owner": "1", "path": ["312/1037/1038"], "permalink_uri": "http://hdl.handle.net/2237/10456", "pubdate": {"attribute_name": "PubDate", "attribute_name_i18n": "\u516c\u958b\u65e5", "attribute_value": "2008-08-26"}, "publish_date": "2008-08-26", "publish_status": "0", "recid": "8705", "relation": {}, "relation_version_is_last": true, "title": ["MODELING OF INDIVIDUALITIES IN DRIVING THROUGH SPECTRAL ANALYSIS OF BEHAVIORAL SIGNALS"], "weko_shared_id": null}