In this paper, we present C-FAR, a framework for reasoning about anomalies in road-based intelligent transportation systems (ITS) based on video monitoring by the roadside camera infrastructure. The anomalies could span broad temporal and spatial ranges, including fine-grain (e.g., unsafe interactions among moving vehicles in real-time), medium-grain (e.g., aggressive/unsafe driving styles of individual vehicles over extended periods/distances), and coarse-grain (e.g., ensemble properties of the traffic over even longer time horizons). Unlike traditional approaches that utilize deep learning to recognize individual activities, C-FAR does so only for primitive movements and activities and then builds a comprehensive event logic framework. It also provides an optimal resolution of the detected/predicted anomalies by identifying the minimal changes in the controllable parameters of the system. We implemented a prototype system and tested it on three distinct real-world traffic data sets. We demonstrate that the proposed scheme can predict anomalies with over 84% recall level at 95% confidence level approximately 4.05 seconds before the incident.
C-FAR: A Compositional Framework for Anomaly Resolution in Intelligent Transportation Systems
IEEE Transactions on Intelligent Transportation Systems ; 24 , 1 ; 1015-1024
2023-01-01
2411828 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Argonne Simulation Framework for Intelligent Transportation Systems
British Library Conference Proceedings | 1996
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