Skip to Main content Skip to Navigation
Conference papers

Estimating the Driver Status Using Long Short Term Memory

Abstract : 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 papers
Complete list of metadata

Cited literature [29 references]  Display  Hide  Download

https://hal.inria.fr/hal-02520060
Contributor : Hal Ifip <>
Submitted on : Thursday, March 26, 2020 - 1:52:00 PM
Last modification on : Tuesday, March 31, 2020 - 3:50:23 PM
Long-term archiving on: : Saturday, June 27, 2020 - 2:21:26 PM

File

 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2022-01-01

Please log in to resquest access to the document

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Shokoufeh Monjezi Kouchak, Ashraf Gaffar. Estimating the Driver Status Using Long Short Term Memory. 3rd International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE), Aug 2019, Canterbury, United Kingdom. pp.67-77, ⟨10.1007/978-3-030-29726-8_5⟩. ⟨hal-02520060⟩

Share

Metrics

Record views

66