Current smartphone-based navigation applications fail to provide lane-level information due to poor GPS accuracy. Detecting and tracking a vehicle\u2019s lane position on the road assists in lane-level navigation. For instance, it would be important to know whether a vehicle is in the correct lane for safely making a turn, or whether the vehicle\u2019s speed is compliant with a lane-specific speed limit. Recent efforts have used road network information and inertial sensors to estimate lane position. While inertial sensors can detect lane shifts over short windows, it would suffer from error accumulation over time. In this article, we present DeepLane, a system that leverages the back camera of a windshield-mounted smartphone to provide an accurate estimate of the vehicle\u2019s current lane. We employ a deep learning–based technique to classify the vehicle\u2019s lane position. DeepLane does not depend on any infrastructure support such as lane markings and works even when there are no lane markings, a characteristic of many roads in developing regions. We perform extensive evaluation of DeepLane on real-world datasets collected in developed and developing regions. DeepLane can detect a vehicle\u2019s lane position with an accuracy of over 90%, and we have implemented DeepLane as an Android app.<\/p>\n","protected":false},"excerpt":{"rendered":"
Current smartphone-based navigation applications fail to provide lane-level information due to poor GPS accuracy. Detecting and tracking a vehicle\u2019s lane position on the road assists in lane-level navigation. For instance, it would be important to know whether a vehicle is in the correct lane for safely making a turn, or whether the vehicle\u2019s speed is […]<\/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":[13556,13547,13568],"msr-publication-type":[193715],"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-689655","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-systems-and-networking","msr-research-area-technology-for-emerging-markets","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2020-1-2","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"ACM Transactions on Sensor Networks","msr_volume":"16","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"1","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":"doi","viewUrl":"false","id":"false","title":"https:\/\/doi.org\/10.1145\/3358797","label_id":"243109","label":0},{"type":"file","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2020\/09\/Nambi_DeepLane_TOSN2020.pdf","id":"689664","title":"nambi_deeplane_tosn2020","label_id":"243103","label":0}],"msr_related_uploader":"","msr_attachments":[{"id":689664,"url":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2020\/09\/Nambi_DeepLane_TOSN2020.pdf"},{"id":689661,"url":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2020\/09\/InSight_UBICOMP2020.pdf"}],"msr-author-ordering":[{"type":"text","value":"Ravi Bhandari","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":"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"},{"type":"text","value":"Bhaskaran Raman","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[199562],"msr_event":[],"msr_group":[144725,144939,602169],"msr_project":[320399],"publication":[],"video":[],"download":[],"msr_publication_type":"article","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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