Heterogeneity in the Driver Behavior: An Exploratory Study Using Real-Time Driving Data

Yarlagadda, Jahnavi and Pawar, Digvijay S (2022) Heterogeneity in the Driver Behavior: An Exploratory Study Using Real-Time Driving Data. Journal of Advanced Transportation, 2022. pp. 1-17. ISSN 0197-6729

[img] Text
Advanced_Transportation_.pdf - Published Version
Available under License Creative Commons Attribution.

Download (5MB)


Driver behavior heterogeneity is a significant aspect to understand the individual behavioral variations and develop driver assistance systems. This study characterizes the heterogeneity in driving behavior using real-time driving performance features. In this context, the study investigates the extent of variations in the individual's driving styles during routine driving. The driving styles are conceptualized using the vehicle kinematic data, that is, speed and accelerations performed during longitudinal control. The data is collected for 42 professional drivers using instrumented vehicle over a defined study stretch. An algorithm is developed for data extraction and total 7548 acceleration and 6156 braking maneuvers and corresponding driving performance features are extracted. The driving maneuver data are analyzed using the unsupervised techniques (PCA and K-means clustering) and three patterns of acceleration and braking are identified, which are further associated with two patterns of speed behavior. The results showed that each driver is found to exhibit different driving patterns in different driving regimes and no driver shows constantly safe or aggressive behavior. The aggression scores are found to be different among drivers, indicating the behavioral heterogeneity. This study results demonstrate that, driver's level of aggression in different driving regimes is not constant and characterizing the driver by means of abstract driving features is not indicative of the diversified driving behavior. The proposed method identifies the individualized driving behaviors, reflecting the driver's choice of driving maneuvers. Thus, the insights from the study are highly useful to design driver-specific safety models for driver assistance and driver identification. © 2022 Jahnavi Yarlagadda and Digvijay S. Pawar.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Pawar, Digvijay Shttps://orcid.org/0000-0003-4228-3283
Item Type: Article
Uncontrolled Keywords: Driver's behavior; Driver-assistance systems; Driving behaviour; Driving performance; Driving styles; Exploratory studies; Kinematic data; Professional drivers; Real- time; Time drivings
Subjects: Civil Engineering
Divisions: Department of Civil Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 13 Oct 2022 14:30
Last Modified: 13 Oct 2022 14:30
URI: http://raiith.iith.ac.in/id/eprint/10930
Publisher URL: http://doi.org/10.1155/2022/4509071
OA policy: https://v2.sherpa.ac.uk/id/publication/7484
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 10930 Statistics for this ePrint Item