Driver inattention is one of the leading causes of vehicle crashes and incidents worldwide. Driver inattention includes driver fatigue leading to drowsiness and driver distraction, say due to use of cellphone or rubbernecking, all of which leads to a lack of situational awareness. Hitherto, techniques presented to monitor driver attention evaluated factors such as fatigue and distraction independently. However, in order to develop a robust driver attention monitoring system all the factors affecting driver\u2019s attention needs to be analyzed holistically. In this paper, we present AutoRate, a system that leverages front camera of a windshield-mounted smartphone to monitor driver\u2019s attention by combining several features. We derive a driver attention rating by fusing spatio-temporal features based on the driver state and behavior such as head pose, eye gaze, eye closure, yawns, use of cellphones, etc.<\/p>\n
We perform extensive evaluation of AutoRate on realworld driving data and also data from controlled, static vehicle settingswith30driversinalargecity.Wecompare AutoRate\u2019s automatically-generated rating with the scores given by 5 human annotators. Further, we compute the agreement between AutoRate\u2019s rating and human annotator rating using kappa coef\ufb01cient. AutoRate\u2019s automatically-generated rating has an overall agreement of 0.87 with the ratings provided by 5 human annotators on the static dataset.<\/p>\n","protected":false},"excerpt":{"rendered":"
Driver inattention is one of the leading causes of vehicle crashes and incidents worldwide. Driver inattention includes driver fatigue leading to drowsiness and driver distraction, say due to use of cellphone or rubbernecking, all of which leads to a lack of situational awareness. Hitherto, techniques presented to monitor driver attention evaluated factors such as fatigue […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13554,13547],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-589213","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-human-computer-interaction","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"IEEE","msr_edition":"","msr_affiliation":"","msr_published_date":"2019-5-1","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2019\/05\/AutoRate-FG2019.pdf","id":"589216","title":"autorate-fg2019","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[{"id":589216,"url":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2019\/05\/AutoRate-FG2019.pdf"}],"msr-author-ordering":[{"type":"text","value":"Isha Dua","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Akshay Nambi","user_id":38169,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Akshay Nambi"},{"type":"text","value":"C. V. Jawahar","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Venkat Padmanabhan","user_id":33180,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Venkat Padmanabhan"}],"msr_impact_theme":[],"msr_research_lab":[199562],"msr_event":[],"msr_group":[144725,602169],"msr_project":[320399],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":320399,"post_title":"HAMS: Harnessing AutoMobiles for Safety","post_name":"hams","post_type":"msr-project","post_date":"2016-11-12 07:23:49","post_modified":"2022-07-18 06:11:34","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/hams\/","post_excerpt":"In the Harnessing AutoMobiles for Safety (HAMS) project, we use low-cost sensing devices to construct a virtual harness for vehicles. 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