Hybrid-state driver/vehicle modelling, estimation and prediction

The first part of this study develops a general architecture for estimation and prediction of hybrid-state systems. The proposed system utilizes the hybrid characteristics of decision-behaviour coupling of many systems such as the driver and the vehicle; uses estimates of observable parameters to track instantaneous discrete state and predicts the most likely outcome, depending on the discrete model and the observed behaviour of the continuous subsystem. The proposed method is suitable for the scenarios that involve unknown decisions of other individuals, such as lane changes or intersection precedence/access. In the second part, this paper specifically deals with the implementation of the proposed methodology on an intersection safety system, predicting the vehicle behaviours and potential outcomes through traffic intersection scenarios. Driver intentions are tracked and predicted through vehicle behaviour, and possible combinations of intention predictions for different vehicles are interpreted for the safety of the situation.