{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T10:06:13Z","timestamp":1773655573491,"version":"3.50.1"},"reference-count":108,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2021,8,16]],"date-time":"2021-08-16T00:00:00Z","timestamp":1629072000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJPCC"],"published-print":{"date-parts":[[2023,5,22]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>This study aims to analyze driver risks in the driving environment. A complete analysis of context aware assistive driving techniques. Context awareness in assistive driving by probabilistic modeling techniques. Advanced techniques using Spatio-temporal techniques, computer vision and deep learning techniques.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>Autonomous vehicles have been aimed to increase driver safety by introducing vehicle control from the driver to Advanced Driver Assistance Systems (ADAS). The core objective of these systems is to cut down on road accidents by helping the user in various ways. Early anticipation of a particular action would give a prior benefit to the driver to successfully handle the dangers on the road. In this paper, the advancements that have taken place in the use of multi-modal machine learning for assistive driving systems are surveyed. The aim is to help elucidate the recent progress and techniques in the field while also identifying the scope for further research and improvement. The authors take an overview of context-aware driver assistance systems that alert drivers in case of maneuvers by taking advantage of multi-modal human processing to better safety and drivability.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>There has been a huge improvement and investment in ADAS being a key concept for road safety. In such applications, data is processed and information is extracted from multiple data sources, thus requiring training of machine learning algorithms in a multi-modal style. The domain is fast gaining traction owing to its applications across multiple disciplines with crucial gains.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Research limitations\/implications<\/jats:title>\n<jats:p>The research is focused on deep learning and computer vision-based techniques to generate a context for assistive driving and it would definitely adopt by the ADAS manufacturers.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Social implications<\/jats:title>\n<jats:p>As context-aware assistive driving would work in real-time and it would save the lives of many drivers, pedestrians.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>This paper provides an understanding of context-aware deep learning frameworks for assistive driving. The research is mainly focused on deep learning and computer vision-based techniques to generate a context for assistive driving. It incorporates the latest state-of-the-art techniques using suitable driving context and the driver is alerted. Many automobile manufacturing companies and researchers would refer to this study for their enhancements.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ijpcc-11-2020-0192","type":"journal-article","created":{"date-parts":[[2021,8,15]],"date-time":"2021-08-15T09:50:56Z","timestamp":1629021056000},"page":"325-342","source":"Crossref","is-referenced-by-count":4,"title":["Context\u2013aware assistive driving: an overview of techniques for mitigating the risks of driver in real-time driving environment"],"prefix":"10.1108","volume":"19","author":[{"given":"Shilpa","family":"Gite","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ketan","family":"Kotecha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gheorghita","family":"Ghinea","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2021,8,16]]},"reference":[{"key":"key2024081912183904300_ref001","first-page":"304","article-title":"Towards a better understanding of context and context-awareness","year":"1999"},{"issue":"1","key":"key2024081912183904300_ref002","first-page":"270","article-title":"Drowsy driver identification using eye blink detection","volume":"6","year":"2015","journal-title":"IJISET-International Journal of Computer Science and Information Technologies"},{"key":"key2024081912183904300_ref003","article-title":"Decision-making framework for using ambient assisted living","year":"2020","journal-title":"International Journal of Pervasive Computing and Communications"},{"key":"key2024081912183904300_ref0300","first-page":"1","article-title":"Decision anticipation for driving assistance systems","year":"2020","journal-title":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), IEEE"},{"issue":"1\/2","key":"key2024081912183904300_ref004","first-page":"79","article-title":"A probabilistic modelling system for assessing flood risks","volume":"38","year":"2006","journal-title":"Natural Hazards"},{"issue":"1","key":"key2024081912183904300_ref005","first-page":"185","article-title":"State-of-the-art in visual attention modeling","volume":"35","year":"2012","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"key2024081912183904300_ref006","first-page":"1","article-title":"Robust facial feature tracking","year":"2000","journal-title":"BMVC"},{"issue":"3","key":"key2024081912183904300_ref007","first-page":"225","article-title":"Safety assessment of driver assistance systems","volume":"1","year":"2001","journal-title":"European Journal of Transport and Infrastructure Research"},{"key":"key2024081912183904300_ref008","first-page":"507","article-title":"Exploration of explainable ai in context of human-machine interface for the assistive driving system","volume-title":"Asian Conference on Intelligent Information and Database Systems, Pages","year":"2020"},{"key":"key2024081912183904300_ref009","first-page":"88","article-title":"3d head pose estimation without feature tracking","volume-title":"Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition, IEEE","year":"1998"},{"issue":"3","key":"key2024081912183904300_ref010","article-title":"Situation, activity and goal awareness in ubiquitous computing","volume":"8","year":"2012","journal-title":"International Journal of Pervasive Computing and Communications"},{"issue":"3","key":"key2024081912183904300_ref011","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support vector machine","volume":"20","year":"1995","journal-title":"Machine Learning"},{"key":"key2024081912183904300_ref012","first-page":"2507","article-title":"Eye-gaze detection with a single webcam based on geometry features extraction","volume-title":"2010 11th International Conference on Control Automation Robotics and Vision, IEEE","year":"2010"},{"key":"key2024081912183904300_ref013","article-title":"Road accident classification data","year":"2016"},{"key":"key2024081912183904300_ref014","article-title":"Brain4Cars","year":"2019"},{"issue":"7","key":"key2024081912183904300_ref015","doi-asserted-by":"crossref","first-page":"2051","DOI":"10.1109\/TITS.2016.2535402","article-title":"Where does the driver look? 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