DriBe: on-Road Mobile Telemetry for Locality-Neutral Driving Behavior Annotation

Monitoring driving behavior is essential to ensure on-road safety. Although driving is a collective, cooperative task among the drivers of the neighboring vehicles, existing platforms for driving behavior analysis solely rely on different on-road maneuvers taken by a driver. By analyzing a large volume of publicly available data over two countries and in-house collected data, this paper argues that analyzing driving behavior needs treatment over different factors which compel a driver to take maneuvers that are otherwise recommended to be avoided. Consequently, we develop DriBe. This smartphone-based pervasive sensing system utilizes video, GPS, and inertial sensor data to investigate the causes and consequences of driving maneuvers to score a driver based on a thorough understanding of their on-road driving behavior. Considering that the causality factors are very much specific to a particular driving environment (like a country), DriBe also incorporates a domain-adaptive architecture by utilizing a transfer learning framework. Thorough evaluation of DriBe with datasets from three countries shows that a score based on such causal factors provides a more accurate representation of driving behavior compared to baselines.