Scotto: Real-Time Driver Behavior Scoring Using In-Vehicle Data

摘要

This paper explores the minimal data-set to be collected from vehicles, to efficiently score drivers, using common vehicle sensors. This can lead to important results for insurance companies, advertisements and personalization. Existing work relies on several sensor information that are collected over a drive including acceleration/deceleration patterns or average trip duration. Vehicular companies make vehicular sensor information available to many external services. To explore how to score driving behaviors from such a data, we consider a system that interfaces to vehicle bus and executes supervised learning methods on this data. To facilitate this analysis, we collect in vehicle data from 20 drivers on a test route and have less than %10 error in our scoring algorithm.