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Non-intrusive Distracted Driving Detection Based on Driving Sensing Data
Jafarnejad, Sasan; Castignani, German; Engel, Thomas
2018In 4th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2018)
Peer reviewed
 

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Keywords :
distraction; driver behavior; innattention; machine learning; driver modeling
Abstract :
[en] Nowadays Internet-enabled phones have become ubiquitous, and we all witness the flood of information that often arrives with a notification. Most of us immediately divert our attention to our phones even when we are behind the wheel. Statistics show that drivers use their phone on 88% of their trips and on 2015 in the UnitedKingdom 25% of the fatal accidents were caused by distraction or impairment. Therefore there is need to tackle this issue. However, most of the distraction detection methods either use expensive dedicated hardware and/or they make use of intrusive or uncomfortable sensors. We propose distracted driving detection mechanism using non-intrusive vehicle sensor data. In the proposed method 9 driving signals are used. The data is collected, then two sets of statistical and cepstral features are extracted using a sliding window process, further a classifier makes a prediction for each window frame, lastly, a decision function takes the last l predictions and makes the final prediction. We evaluate the subject independent performance of the proposed mechanism using a driving dataset consisting of 13 drivers. We show that performance increases as the decision window become larger.We achieve the best results using a Gradient Boosting classifier with a decision window of total duration 285seconds which yield ROC AUC of 98.7%.
Disciplines :
Computer science
Author, co-author :
Jafarnejad, Sasan ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Castignani, German ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Engel, Thomas ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
External co-authors :
no
Language :
English
Title :
Non-intrusive Distracted Driving Detection Based on Driving Sensing Data
Publication date :
March 2018
Event name :
4th International Conference on Vehicle Technology and Intelligent Transport Systems
Event place :
Funchal, Portugal
Event date :
from 16-03-2018 to 18-03-2018
Audience :
International
Main work title :
4th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2018)
Peer reviewed :
Peer reviewed
Focus Area :
Security, Reliability and Trust
Available on ORBilu :
since 15 February 2018

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