Faced with increasing congestion on urban roads, authorities need better real-time traffic information to manage traffic. Kalman Filters are efficient algorithms that can be adapted to track vehicles in urban traffic given noisy sensor data. A Kalman Filter process model that approximates dynamic vehicle behaviour is a reusable subsystem for modelling the dynamics of a multi-vehicle traffic system. The challenge is choosing an appropriate process model that produces the smallest estimation errors. This paper provides a comparative analysis and evaluation of Linear and Unscented Kalman Filters process models for urban traffic applications.
Kalman filter process models for urban vehicle tracking
01.10.2009
1489616 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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