Scipediacontent: Scipediacontent moved page Draft Content 768640779 to Huang et al 2019a

2021-02-03T16:14:18Z

Scipediacontent moved page Draft Content 768640779 to Huang et al 2019a

Scipediacontent: Created page with " == Abstract == "Hands on the wheel, eyes on the road" is the central guideline of safe vehicle driving practices. Many advanced driver assistance systems can effectively det..."

2021-02-03T16:14:15Z

Created page with " == Abstract == "Hands on the wheel, eyes on the road" is the central guideline of safe vehicle driving practices. Many advanced driver assistance systems can effectively det..."

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== Abstract ==

"Hands on the wheel, eyes on the road" is the central guideline of safe vehicle driving practices. Many advanced driver assistance systems can effectively detect abnormal vehicle motions. However, these systems often leave insufficient time for drivers to respond to complex road situations, especially when the drivers are distracted. To reduce accidents, it is essential to detect whether a driver complies with safe driving guidelines in real time and provide warnings early before any dangerous maneuvers occur. There are vision-based driver distraction monitoring systems which rely on cameras in high-end vehicles, but their performances are heavily constrained by visibility requirements. In this paper, we present MagTrack, a driver monitoring system that is based on tracking magnetic tags worn by the user. With a single smartwatch and two low-cost magnetic accessories: a hand magnetic ring and a head magnetic eyeglasses clip, our system tracks and classifies a driver's bimanual and head movements simultaneously using both analytical and approximation sensing models. Our approach is robust to driver's postures, vehicles, and environmental changes. We demonstrate that a wide range of activities can be detected by our system, including bimanual steering, visual and manual distractions, and lane changes and turns. In extensive road tests with 500+ instances of driving activities and 500+ minutes of road driving with 10 subjects, MagTrack achieves 87% of precision and 90% of recall rate on the detection of unsafe driving activities.

== Original document ==

The different versions of the original document can be found in:

* [https://dl.acm.org/doi/pdf/10.1145/3307334.3326107 https://dl.acm.org/doi/pdf/10.1145/3307334.3326107]

* [https://dblp.uni-trier.de/db/conf/mobisys/mobisys2019.html#0003CL19 https://dblp.uni-trier.de/db/conf/mobisys/mobisys2019.html#0003CL19],
: [https://dl.acm.org/citation.cfm?id=3326107 https://dl.acm.org/citation.cfm?id=3326107],
: [https://doi.org/10.1145/3307334.3326107 https://doi.org/10.1145/3307334.3326107],
: [https://academic.microsoft.com/#/detail/2950412189 https://academic.microsoft.com/#/detail/2950412189]

* [https://dl.acm.org/doi/pdf/10.1145/3307334.3326107 https://dl.acm.org/doi/pdf/10.1145/3307334.3326107],
: [http://dx.doi.org/10.1145/3307334.3326107 http://dx.doi.org/10.1145/3307334.3326107] under the license http://www.acm.org/publications/policies/copyright_policy#Background

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Huang et al 2019a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 768640779 to Huang et al 2019a

Scipediacontent: Created page with " == Abstract == "Hands on the wheel, eyes on the road" is the central guideline of safe vehicle driving practices. Many advanced driver assistance systems can effectively det..."