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Bayesian Network Approach to Vehicle Mode Monitoring Using Embedded System with 3-axis Accelerometer

Atis Elsts, Artis Mednis, Leo Selavo

Abstract


Vehicle activity monitoring is an important problem in the transport domain: the activities have impact on the fuel consumption of the vehicle, and therefore its economical feasibility. Activity detection usually is performed using relatively expensive and complicated GNSS receivers. We propose a Bayesian network based algorithm for vehicle mode detection. The algorithm can reliably differentiate between standing and moving modes in a diverse set of test scenarios. It is suitable for a low-cost hardware consisting of low-power microcontroller and uses 3-axis accelerometer as the sole sensor.

Keywords


vehicle, monitoring, embedded, accelerometer, Bayesian network.

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