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Conference Papers Year : 2019

Estimating the Driver Status Using Long Short Term Memory


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.
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Dates and versions

hal-02520060 , version 1 (26-03-2020)


Attribution - CC BY 4.0



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⟩
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