Moving attention from the road: A new methodology for the driver distraction evaluation using machine learning approaches
This work describes an approach to develop a model of driver's distraction induced by an on-board information system basing on vehicle data. Machine learning techniques have been adopted to find the model able to better predict distraction, given a target value. Results pointed in favor of a mo...
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| creator | Tango, F. Calefato, C. Minin, L. Canovi, L. |
| description | This work describes an approach to develop a model of driver's distraction induced by an on-board information system basing on vehicle data. Machine learning techniques have been adopted to find the model able to better predict distraction, given a target value. Results pointed in favor of a model obtained with the ANFIS (adaptive neuro fuzzy inference system) technique. Further investigations will be carried out by porting the model on real car. |
| doi_str_mv | 10.1109/HSI.2009.5091044 |
| format | Conference Proceeding |
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Machine learning techniques have been adopted to find the model able to better predict distraction, given a target value. Results pointed in favor of a model obtained with the ANFIS (adaptive neuro fuzzy inference system) technique. Further investigations will be carried out by porting the model on real car.</description><subject>distraction</subject><subject>driver modeling</subject><subject>Fatigue</subject><subject>Fuzzy systems</subject><subject>Humans</subject><subject>inattention</subject><subject>Machine learning</subject><subject>Predictive models</subject><subject>Road accidents</subject><subject>Road vehicles</subject><subject>Testing</subject><subject>Transportation</subject><subject>Transportation Technology</subject><subject>Vehicle driving</subject><issn>2158-2246</issn><isbn>9781424439591</isbn><isbn>1424439590</isbn><isbn>1424439604</isbn><isbn>9781424439607</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotkFFPwjAUhWuUREDeTXzpHxj2tl23-kaICgnGB3kntb1jNdtKuoHh3zsmDzf3ntyc7ySHkEdgcwCmn1df6zlnTM9TpoFJeUMmILmUQismb8lMZ_lVpxruyJhDmiecSzUik4tP9wPqnsza9ocxJiDPNYcxOX-Ek2_21HQdNp0PDS1iqGlXIo3BuBe6oA3-0hq7MrhQhf2ZFiEOfxf9CSN1vu2isYMXT6Y6muE8thdsbWzpG6QVmtgMOYdDz7Ultg9kVJiqxdl1T8n27XW7XCWbz_f1crFJvGZdIrQtHCBLVZ6ZLDNCGeXAcgCbKc1SyO03R9eXUOQORa_RWpWisAyVE1ZMydM_1iPi7hB9beJ5d21R_AG2n2PM</recordid><startdate>200905</startdate><enddate>200905</enddate><creator>Tango, F.</creator><creator>Calefato, C.</creator><creator>Minin, L.</creator><creator>Canovi, L.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200905</creationdate><title>Moving attention from the road: A new methodology for the driver distraction evaluation using machine learning approaches</title><author>Tango, F. ; Calefato, C. ; Minin, L. ; Canovi, L.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-39cfd1e05687a77a36a6d1c211c7690518cb2ed604f8de3518ecc65e3c0e6d3c3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>distraction</topic><topic>driver modeling</topic><topic>Fatigue</topic><topic>Fuzzy systems</topic><topic>Humans</topic><topic>inattention</topic><topic>Machine learning</topic><topic>Predictive models</topic><topic>Road accidents</topic><topic>Road vehicles</topic><topic>Testing</topic><topic>Transportation</topic><topic>Transportation Technology</topic><topic>Vehicle driving</topic><toplevel>online_resources</toplevel><creatorcontrib>Tango, F.</creatorcontrib><creatorcontrib>Calefato, C.</creatorcontrib><creatorcontrib>Minin, L.</creatorcontrib><creatorcontrib>Canovi, L.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Tango, F.</au><au>Calefato, C.</au><au>Minin, L.</au><au>Canovi, L.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Moving attention from the road: A new methodology for the driver distraction evaluation using machine learning approaches</atitle><btitle>2009 2nd Conference on Human System Interactions</btitle><stitle>HSI</stitle><date>2009-05</date><risdate>2009</risdate><spage>596</spage><epage>599</epage><pages>596-599</pages><issn>2158-2246</issn><isbn>9781424439591</isbn><isbn>1424439590</isbn><eisbn>1424439604</eisbn><eisbn>9781424439607</eisbn><abstract>This work describes an approach to develop a model of driver's distraction induced by an on-board information system basing on vehicle data. Machine learning techniques have been adopted to find the model able to better predict distraction, given a target value. Results pointed in favor of a model obtained with the ANFIS (adaptive neuro fuzzy inference system) technique. Further investigations will be carried out by porting the model on real car.</abstract><pub>IEEE</pub><doi>10.1109/HSI.2009.5091044</doi><tpages>4</tpages></addata></record> |
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| ispartof | 2009 2nd Conference on Human System Interactions, 2009, p.596-599 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | distraction driver modeling Fatigue Fuzzy systems Humans inattention Machine learning Predictive models Road accidents Road vehicles Testing Transportation Transportation Technology Vehicle driving |
| title | Moving attention from the road: A new methodology for the driver distraction evaluation using machine learning approaches |
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