Scipediacontent: Scipediacontent moved page Draft Content 180129155 to Kouchak Gaffar 2019a

2021-02-02T04:22:09Z

Scipediacontent moved page Draft Content 180129155 to Kouchak Gaffar 2019a

Scipediacontent: Created page with " == Abstract == International audience; Driver distraction is one of the leading causes of fatal car accidents. Driver distraction is any task that diverts the driver attenti..."

2021-02-02T04:22:04Z

Created page with " == Abstract == International audience; Driver distraction is one of the leading causes of fatal car accidents. Driver distraction is any task that diverts the driver attenti..."

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== Abstract ==

International audience; Driver distraction is one of the leading causes of fatal car accidents. Driver distraction is any task that diverts the driver attention from the primary task of driving and increases the driver’s cognitive load. Detecting potentially dangerous driving situations or automating some repetitive tasks, using Advanced Driver Assistance Systems (ADAS), and using autonomous vehicles to reduce human errors while driving are two suggested solutions to diminish driver distraction. These solutions have some advantages, but they suffer from their inherent inability to detect all potentially dangerous driving situations. Besides, autonomous vehicles and ADAS depend on sensors. As a result, their accuracy diminishes significantly in adverse conditions. Analyzing driver behavior using machine learning methods and estimating the distraction level of drivers can be used to detect potentially hazardous situations and warn the drivers. We conducted an experiment in eight different driving scenarios and collected a large dataset from driving data and driver related data. We chose Long Short Term Memory (LSTM) as our machine learning method. We built and trained a stacked LSTM network to estimate the driver status using a sequence of driving data vectors. Each driving data vector has 10 driving related features. We can accurately estimate the driver status with no external devices and only using cars Can-Bus data.

Document type: Conference object

== Original document ==

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

* [http://link.springer.com/content/pdf/10.1007/978-3-030-29726-8_5 http://link.springer.com/content/pdf/10.1007/978-3-030-29726-8_5],
: [http://dx.doi.org/10.1007/978-3-030-29726-8_5 http://dx.doi.org/10.1007/978-3-030-29726-8_5] under the license http://creativecommons.org/licenses/by/

* [https://hal.inria.fr/hal-02520060 https://hal.inria.fr/hal-02520060],
: [https://hal.inria.fr/hal-02520060/document https://hal.inria.fr/hal-02520060/document],
: [https://hal.inria.fr/hal-02520060/file/485369_1_En_5_Chapter.pdf https://hal.inria.fr/hal-02520060/file/485369_1_En_5_Chapter.pdf] under the license http://www.springer.com/tdm

* [https://link.springer.com/chapter/10.1007%2F978-3-030-29726-8_5 https://link.springer.com/chapter/10.1007%2F978-3-030-29726-8_5],
: [https://dblp.uni-trier.de/db/conf/cdmake/cdmake2019.html#KouchakG19 https://dblp.uni-trier.de/db/conf/cdmake/cdmake2019.html#KouchakG19],
: [https://hal.inria.fr/hal-02520060 https://hal.inria.fr/hal-02520060],
: [https://academic.microsoft.com/#/detail/2969839024 https://academic.microsoft.com/#/detail/2969839024] under the license http://creativecommons.org/licenses/by/

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Kouchak Gaffar 2019a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 180129155 to Kouchak Gaffar 2019a

Scipediacontent: Created page with " == Abstract == International audience; Driver distraction is one of the leading causes of fatal car accidents. Driver distraction is any task that diverts the driver attenti..."