A Framework for Building Comprehensive Driver Profiles

Abstract

Conventional approaches to modelling driver risk have incorporated measures such as driver gender, age, place of residence, vehicle model, and annual miles driven. However, in the last decade, research has shown that assessing a driver’s crash risk based on these variables does not go far enough—especially as advanced technology changes today’s vehicles, as well as the role and behavior of the driver. There is growing recognition that actual driver usage patterns and driving behavior, when it can be properly captured in modelling risk, offers higher accuracy and more individually tailored projections. However, several challenges make this difficult. These challenges include accessing the right types of data, dealing with high-dimensional data, and identifying the underlying structure of the variance in driving behavior. There is also the challenge of how to identify key variables for detecting and predicting risk, and how to combine them in predictive algorithms. This paper proposes a systematic feature extraction and selection framework for building Comprehensive Driver Profiles that serves as a foundation for driver behavior analysis and building whole driver profiles. Features are extracted from raw data using statistical feature extraction techniques, and a hybrid feature selection algorithm is used to select the best driver profile feature set based on outcomes of interest such as crash risk. It can give rise to individualized detection and prediction of risk, and can also be used to identify types of drivers who exhibit similar patterns of driving and vehicle/technology usage. The developed framework is applied to a naturalistic driving dataset—NEST, derived from the larger SHRP2 naturalistic driving study to illustrate the types of information about driver behavior that can be harnessed—as well as some of the important applications that can be derived from it.

Vehicle Directional Driving Behavior Segmentation Based on Cornering Strength

“…This algorithm is used for clustering the drivers or the driving behaviors with the similar characteristics. Payyanadan et al [26]used K-means clustering algorithm to determine the number of clusters of crash risk features. According to the results, 14 important features that contribute to the pre-crash risk were clustered in 6 risk feature clusters.…”

Driver Profile and Driving Pattern Recognition for Road Safety Assessment: Main Challenges and Future Directions

“…Naturalistic driving data were also used by [33], [34], [35], [36] with almost all of them using speed as a driving metric to be analyze. Acceleration was also used by some of these studies.…”

“…Acceleration was also used by some of these studies. Physiological indicator data such as heart rate and eye movement were also collected by [34] and [35]. Studies [37] and [38] was differentiated by collecting data from an autonomous simulator experiment and an administered questionnaire, respectively.…”

“…Regarding the data analysis and management approach, a conclusion drawn based on this review is that driver profiling studies should include the steps of data management, dimension reduction, feature importance and selection, profile clustering and assessment of results [34]. This suggestion can serve as a basis for future studies to build data strategies and methodologies on it.…”

“…The connection between driving behavior indicators and driving risk can be recognized through several means such as the accident history of the drivers or some indirect metrics that express risk of collision or very aggressive behavior. To summarize, it is recommended for future research to use the proposed integrated methodological framework, that combines the macroscopic and the microscopic driving behavior approach that should constantly be updated [34]. This interaction between the two scientific objectives of driver profiles and driving patterns will provide a more complete understanding of driving behavior.…”