Abstract
This paper describes the modelling of naturalistic driving behaviour in real-world traffic scenarios, based on driving data collected via an experimental automobile equipped with a continuous sensing drive recorder. This paper focuses on the longitudinal driving situations which are classified into five categories – car following, braking, free following, decelerating and stopping – and are referred to as driving states. Here, the model is assumed to be represented by a state flow diagram. Statistical machine learning of driver–vehicle–environment system model based on driving database is conducted by a discriminative modelling approach called boosting sequential labelling method.
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