Please note: This PhD seminar will take place in DC 2568.
Connor
Raymond
Stewart,
PhD
candidate
David
R.
Cheriton
School
of
Computer
Science
Supervisors: Professors Krzysztof Czarnecki, Paulo Alencar
Lecture abstract
A lecture on the key topics discussed in the paper “Classifying travelers’ driving style using basic safety messages generated by connected vehicles: Application of unsupervised machine learning” by Amin Mohammadnazar, Ramin Arvin, and Asad J. Khattak. Explanations for the data and methodology used by the authors is to be presented along with references to their research.
Article abstract
Driving style can substantially impact mobility, safety, energy consumption, and vehicle emissions. While a range of methods has been used in the past for driving style classification, the emergence of connected vehicles equipped with communication devices provides a new opportunity to classify driving style using high-resolution (10 Hz) microscopic real-world data.
In this study, location-based big data and machine learning are used to classify driving styles ranging from aggressive to calm. This classification can be used to customize driver assistance systems, assess mobility, crash risk, fuel consumption, and emissions. This study’s main objective is to develop a framework that harnesses Basic Safety Messages (BSMs) generated by connected vehicles to quantify instantaneous driving behavior and classify driving styles in different spatial contexts using unsupervised machine learning methods. To this end, a subset of the Safety Pilot Model Deployment (SPMD) with more than 27 million BSM observations generated by more than 1300 individuals making trips on diverse roadways and through several neighborhoods in Ann Arbor, Michigan, were processed and analyzed.
To quantify driving style, the concept of temporal driving volatility, as a surrogate safety measure of unsafe driving behavior, was utilized and applied to vehicle kinematics, i.e., observed speeds and longitudinal/lateral accelerations. Specifically, six volatility measures are extracted and used for classifying drivers. K-means and Kmedoids methods are applied for grouping drivers in aggressive, normal, and calm clusters. Clustering results indicate that not only does driving style vary among drivers, but the thresholds for aggressive and calm driving vary across different roadway types due to variations in environment and road conditions. The proportion of aggressive driving styles was also higher on commercial streets than on highways and residential streets. Notably, we propose a Driving Score to measure driving performance consistently across drivers.