A method for detecting drivers' intentions is essential to facilitate operating mode transitions between driver and driver assistance systems. We propose a driver behavior recognition method using Hidden Markov Models (HMMs) to characterize and detect driving maneuvers and place it in the framework of a cognitive model of human behavior. HMM-based steering behavior models for emergency and normal lane changes as well as for lane keeping were developed using a moving base driving simulator. Analysis of these models after training and recognition tests showed that driver behavior modeling and recognition of different types of lane changes is possible using HMMs.

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Summary: This paper describes an experimental study concerning an evaluation of advanced driving-assistance systems using methods for estimating workload levels. The effects of such systems on drivers' mental workload and driving performance were measured experimentally using the driving simulator. Six subjects were instructed to drive the simulator in a highway environment with and without Adaptive Cruise Control (ACC) and/or the collision-warning system (CWS). To assess the effectiveness of these systems on drivers' performance, the subjects were asked to calculate sums of single-or double-digit figures displayed. The results show that higher accuracy was obtained under a condition with ACC than without it. To estimate the subjects' mental workload levels, their electrocardiograms and respiration data were recorded during the sessions and the RRI, heart rate variance and respiration frequency were calculated. The results indicate that the provision of the CWS and ACC reduced the subjects' mental workload compared with the situation without the systems.

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