  Driving is a complex task carried out under the influence of diverse spatial
objects and their temporal interactions. Therefore, a sudden fluctuation in
driving behavior can be due to either a lack of driving skill or the effect of
various on-road spatial factors such as pedestrian movements, peer vehicles'
actions, etc. Therefore, understanding the context behind a degraded driving
behavior just-in-time is necessary to ensure on-road safety. In this paper, we
develop a system called \ourmethod{} that exploits the information acquired
from a dashboard-mounted edge-device to understand the context in terms of
micro-events from a diverse set of on-road spatial factors and in-vehicle
driving maneuvers taken. \ourmethod{} uses the live in-house testbed and the
largest publicly available driving dataset to generate human interpretable
explanations against the unexpected driving events. Also, it provides a better
insight with an improved similarity of $80$\% over $50$ hours of driving data
than the existing driving behavior characterization techniques.
