TY - GEN
T1 - Data-driven Robust Scoring Approach for Driver Profiling Applications
AU - Abdelrahman, Abdalla
AU - Hassanein, Hossam S.
AU - Abu-Ali, Najah
N1 - Funding Information:
This work is funded by project no. 31R014-Research Center RTTSRC-4-2013 provided by the Roadway Transportation & Traffic Safety Research Center, United Arab Emirates University. This research is also supported by a grant from the Natural Sciences and Engineering Research Council of Canada (NSERC) under grant number: STPGP 479248.
Publisher Copyright:
© 2018 IEEE.
PY - 2018
Y1 - 2018
N2 - Driving behavior profiling has important relevance in many driving applications. For instance, car insurance companies have been recently applying a new insurance paradigm in which a driver's insurance premium is adapted based on realtime driving behavior. Driver profiling process is composed of two sub processes. The first is the detection of certain driving behaviors by acquiring data from onboard devices such as smartphones and OBDII units, whereas the second is the scoring process in which the detected behaviors are used to measure the actual driving risk. The scoring process has been viewed as an intricate problem due to the lack of reliable and large-scale datasets that can provide statistically trustworthy insights. This paper presents a data-driven approach for calculating a driver's risk score by utilizing the SHRP2 naturalistic driving dataset, which is the largest dataset of its kind to date. Two machine learning algorithms, which are support vector regression (SVR) and decision tree regression (DTR) are trained to reflect a driver's score. Driver's score is quantified in terms of the additive inverse of the predicted risk probability. After data filtering and preprocessing, models are trained using thirteen predictors, which represent twelve unique driving behaviors and the total driving time per driver. Validation results show that risk probability can be accurately predicted using the proposed models.
AB - Driving behavior profiling has important relevance in many driving applications. For instance, car insurance companies have been recently applying a new insurance paradigm in which a driver's insurance premium is adapted based on realtime driving behavior. Driver profiling process is composed of two sub processes. The first is the detection of certain driving behaviors by acquiring data from onboard devices such as smartphones and OBDII units, whereas the second is the scoring process in which the detected behaviors are used to measure the actual driving risk. The scoring process has been viewed as an intricate problem due to the lack of reliable and large-scale datasets that can provide statistically trustworthy insights. This paper presents a data-driven approach for calculating a driver's risk score by utilizing the SHRP2 naturalistic driving dataset, which is the largest dataset of its kind to date. Two machine learning algorithms, which are support vector regression (SVR) and decision tree regression (DTR) are trained to reflect a driver's score. Driver's score is quantified in terms of the additive inverse of the predicted risk probability. After data filtering and preprocessing, models are trained using thirteen predictors, which represent twelve unique driving behaviors and the total driving time per driver. Validation results show that risk probability can be accurately predicted using the proposed models.
KW - Internet of vehicles (IoV)
KW - data driven applications
KW - driving behavior profiling
KW - intelligent transportation systems (ITS)
KW - machine learning
KW - prediction models
UR - http://www.scopus.com/inward/record.url?scp=85063457946&partnerID=8YFLogxK
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U2 - 10.1109/GLOCOM.2018.8647971
DO - 10.1109/GLOCOM.2018.8647971
M3 - Conference contribution
AN - SCOPUS:85063457946
T3 - 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings
BT - 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Global Communications Conference, GLOBECOM 2018
Y2 - 9 December 2018 through 13 December 2018
ER -