Assessing safety critical driving patterns of heavy passenger vehicle drivers using instrumented vehicle data – An unsupervised approach

Yarlagadda, Jahnavi and Jain, Pranjal and Pawar, Digvijay S. (2021) Assessing safety critical driving patterns of heavy passenger vehicle drivers using instrumented vehicle data – An unsupervised approach. Accident Analysis & Prevention, 163. pp. 1-14. ISSN 0001-4575

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Assessing the individual's driving profile and identifying the at-fault behaviors contributes to road safety, riding comfort, and driver assistance systems. This study proposes a framework to identify aggressive driving patterns in longitudinal control using real-time driving profiles of heavy passenger vehicle (HPV) drivers. The main objective is to detect and quantify the instantaneous driving decisions and classify the identified maneuvers (acceleration, braking) using unsupervised machine learning techniques without any prior-ground truth. To this end, total 8295 acceleration events, and 7151 braking events, were extracted from 142 driving profiles collected using high-resolution (10 Hz) GPS instrumentation. The principal component analysis was conducted on a multi-dimensional feature set, followed by a two-stage k-means clustering on the reduced feature subspace. The results showed that 86.5% of accelerations and 65.3% of braking maneuvers were characterized as non-aggressive, indicating safe or base-line driving behavior. However, 13.5% of accelerations and 34.7% of braking maneuvers were featured to be aggressive, indicative of the actual risky behaviors. Further analysis demonstrated the heterogeneity in drivers’ trip-level frequency of aggressive maneuvers and highlighted the need for a continuous driving assessment. The study also revealed that the thresholds derived from the obtained clusters featuring the aggressive accelerations (+0.3 to +0.48 g) and aggressive braking (−0.42 to −0.27 g) maneuvers were beyond the acceptable limits of passenger safety and comfort. The insights from the study aids in developing driver assistance systems for personalized feedback provision and improve driver behavior. © 2021 Elsevier Ltd

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IITH Creators:
IITH CreatorsORCiD
Pawar, Digvijay S.
Item Type: Article
Additional Information: This study was funded by the Science and Engineering Research Board, Department of Science and Technology, Government of India (ECR/2018/002407).
Uncontrolled Keywords: Acceleration profiles; Aggressive driving; Driving behavior; K-means clustering; Naturalistic driving data; Speed profiles
Subjects: Electrical Engineering > Electrical and Electronic
Civil Engineering
Divisions: Department of Civil Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 05 Aug 2022 09:38
Last Modified: 05 Aug 2022 09:38
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