Reference : Non-intrusive Distracted Driving Detection Based on Driving Sensing Data
Scientific congresses, symposiums and conference proceedings : Unpublished conference
Engineering, computing & technology : Computer science
Security, Reliability and Trust
Non-intrusive Distracted Driving Detection Based on Driving Sensing Data
Jafarnejad, Sasan mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Castignani, German mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Engel, Thomas mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
4th International Conference on Vehicle Technology and Intelligent Transport Systems
from 16-03-2018 to 18-03-2018
[en] distraction ; driver behavior ; innattention ; machine learning ; driver modeling
[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%.
Researchers ; Professionals ; Students ; General public

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