This paper proposes the hybrid system model identified by a PWARX (piecewise affine autoregressive exogenous) model for modeling human driving behavior. In the proposed model, the mode segmentation is carried out automatically and the optimal number of modes is decided by a novel methodology based on consistent variable selection. In addition, model flexibility is added within the ARX (autoregressive exogenous) partitions in the form of statistical variable selection. The proposed method is able to capture both the decision-making and motion-control facets of the driving behavior. The resulting model is an optimal basal model which is not affected by the choice of data, where the explanatory variables are allowed to vary within each ARX region, thus, allowing a higher-level understanding of the motion-control aspect of the driving behavior, as well as explaining the driver’s decision-making. The proposed model is applied to model the car-following driving task based on real-road driving data, as well as to ROS-CARLA-based car-following simulation and compared to Gipp’s driver model. Obtained results that show better performance both on prediction performance and mimicking actual real-road driving demonstrates and validates the usefulness of the model.

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This paper investigates the decision-making characteristics of the driver in the overtaking on the highway road. For the research purpose, a novel method was proposed by introducing a logistic regression model accompanied by the statistical test technique, which does not require prior knowledge about the explanatory variables. This study hypothesizes that the driver's gazing behavior is crucial for the decision-making process in driving and hence, the line-of-sight information was introduced to estimate driver's gazing behavior in the model of driver's decision specifically for reproducing the overtaking driving behavior accurately. Consequently, the proposed model realized a high describability on the decision of the driver when performing the overtaking driving task, which is one of the significant advancements of the present study with respect to the past similar studies. This study integrates the perspectives of intelligent vehicle design and cognitive science by revealing which factor the driver pays attention to in a changeable driving environment due to various observable factors. In experiments based on the driving simulator with six human subjects, the overtaking behavior was successfully estimated by specifying a set of variables to reconstruct the driver's behavior and then the proposed model provided a minimum set of necessary variables accompanied with key coefficients. In conclusion, the proposed approach based on a simple logistic regression model demonstrated driving behaviors with an accurate estimation of the driver's intention without the need for prior knowledge, and it may contribute to higher describability for various driving actions in a dynamic environment.

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