Modeling and analysis of driver behavior under shared control through weighted visual and haptic guidance
This work addresses the need for better driver models in designing haptic guidance systems for automotive automation, but it is incremental as it builds on existing shared control concepts.
The paper tackled the problem of modeling driver behavior in shared control systems by developing a driver model that weights visual and haptic guidance for lane-following, achieving satisfactory goodness of fit in matching driver input torque among fourteen participants and effectively predicting trajectories.
For the optimum design of a driver-automation shared control system, an understanding of driver behavior based on measurements and modeling is crucial early in the development process. This paper presents a driver model through a weighting process of visual guidance from the road ahead and haptic guidance from a steering system for a lane-following task. The proposed weighting process describes the interaction of a driver with the haptic guidance steering and the driver reliance on it. A driving simulator experiment is conducted to identify the model parameters for driving manually and with haptic guidance. The proposed driver model matched the driver input torque with a satisfactory goodness of fit among fourteen participants after considering the individual differences. The validation results reveal that the simulated trajectory effectively followed the driving course by matching the measured trajectory, thereby indicating that the proposed driver model is capable of predicting driver behavior during haptic guidance. Furthermore, the effect of different degrees of driver reliance on driving performance is evaluated considering various driver states and with system failure via numerical analysis. The model evaluation results reveal the potential of the proposed driver model to be applied in the design and evaluation of a haptic guidance system.