Identification and validation of lateral driver models on experimentally induced driving behavior

This paper presents a real-world driving experiment with aim on controlled variation of steering and lane keeping behavior and investigates the ability of three common driver models to distinguish variations in driving performance. Nine drivers executed a lane keeping task with visual occlusion of the upper or lower field of view restraining them to near or far road scene information. Three common driver models are applied to replicate driving behavior. An autoregressive model with exogeneous input (ARX) is identified using vehicle lateral lane deviation as input and steering wheel angle as output. Two output error models are identified using vehicle heading deviation angles with respect to near and far preview points as respective inputs and steering wheel angle as output. The results show that the driving behaviors induced in the experiment are significantly different in terms of lane keeping performance. In simulations, the output error models exhibit advantages over the ARX model in capturing driving behavior. However, the model natural frequency and the model simulation error show weak performance in discerning this varying driving behavior and are largely determined by track effects.