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

DriCon: On-device Just-in-Time Context Characterization for Unexpected Driving Events