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

• Published in: Proceedings of the IEEE International Conference on Pervasive Computing and Communications pp. 12 - 21
• Main Authors: Das, Debasree, Chakraborty, Sandip, Mitra, Bivas
• Format: Conference Proceeding
• Language: English
• Published: IEEE 13.03.2023
• Subjects: Behavioral sciences; context analysis; Driving behavior; Fluctuations; Intelligent systems; Pervasive computing; Safety; spatial events; Spatiotemporal phenomena; Task analysis
• ISSN: 2474-249X
• DOI: 10.1109/PERCOM56429.2023.10099058

Driving is a complex task carried out under the influence of diverse spatial objects and their temporal inter-actions. 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 DriCon 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. DriCon 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.