Driver characterization & driver specific trajectory planning: an inverse optimal control approach

To achieve a high acceptance by drivers Advanced Driver Assistance Systems (ADAS) have to consider the individual driver behavior. For example an ADAS should not intervene in a situation where drivers purposely cross road markings in harmless situations. To address this issue, a driver behavior characterization can be used to predict a driver´s future trajectory based on his past behavior. In this paper, we present a method for driver behavior classification based on an inverse dynamic optimal approach and show how it can be applied to predict driver specific trajectories. The presented algorithm consists of two phases. In the trainingphase, a description of the driver´s behavior is established using a set of generic driver characteristics. Hereby, a specific driver is described by his individual weighting of the characteristics and additional parameters used in the characteristics. In the prediction-phase, the model is applied to a specific track predicting the driver´s future behavior. The model is adaptable to different situations and modeling purposes. It is shown by simulations that the approach is suited to model drivers with different driving characteristics and that the driver parameters can be reliably identified from recorded trajectories.