Detecting aggressive driving patterns in drivers using vehicle sensor data. (June 2022)

• Record Type: Journal Article
• Authors: Monselise, Michal; Yang, Christopher C.
• Is Part Of: Transportation research interdisciplinary perspectives. Volume 14(2022)
• Publication Date: 2022-06
• Subjects: Aggressive driving -- ADHD drivers -- Naturalistic driving study -- Time series -- KNearest neighbors; Transportation -- Periodicals; 388.05
• DOI: 10.1016/j.trip.2022.100625
• Languages: English
• ISSNs: 2590-1982

Abstract

Highlights: This research aims to find patterns in aggressive driving using machine learning and visualization. 8 raw and derived variables were extracted from SHRP2 (second strategic highway research program). Using the 8 variables, we measured the distance between trips using the Eros distance metric and visualized using dimension reduction. 4 aggressive driving patterns were labeled. The labels were used in a KNN model. Abstract: Aggressive driving is known to be a cause of vehicle accidents. Individuals with Attention-deficit hyperactivity disorder (ADHD) are prone to more aggressive behavior and that also leads to aggressive driving. To prevent aggressive driving, we strive to first understand aggressive driving and find patterns in this type of driving behavior. In an effort to uncover to identify patterns in aggressive driving, we examine sensor data and video data of trips taken by drivers with ADHD and identify our distinct aggressive driving patterns. Using the sensor data, we extend our findings to all aggressive trips in our dataset and generate a model to detect aggressive driving patterns. By finding the similarity between trips and then using these distances to produce a KNN model, we are able to model our data and classify it into 4 driving patterns. This analysis can better inform us of the type of driving patterns that appear in aggressive driving. Using this analysis, we can also better understand which patterns are produce better precision and recall using this methodology.