Modeling intradriver steering variability based on sensorimotor control theories

The purpose of this study is to develop and validate a human-like steering model that can capture, not only the mean, but also the intradriver variability (IDV) of steering behavior, in both routine and emergency scenarios. The IDV model proposed in this study is based on the assumption that steering behavior, in both scenarios, is governed by the same principles as performing point-to-point reaching tasks. The optimal feedback control framework that models the reaching tasks, and the presence of signal-dependent noise in motor commands and sensory feedback, are the mainstays of the proposed model. The driver is assumed to have acquired an internal model of system (muscles, arms, and vehicle) dynamics, and has a preview of the upcoming road. The model is validated using simulator-based data from both routine (curve negotiation) and emergency (obstacle avoidance) scenarios. The IDV model could capture mean steering torque behavior in both routine (variance accounted for (VAF) <formula><tex>$=$</tex></formula> 92<formula><tex>$\%$</tex></formula>) and emergency (VAF <formula><tex>$=$</tex></formula> 74<formula><tex>$\%$</tex></formula>) scenarios, but more prominently, it could capture the standard deviation of the steering torque as well, in both routine (VAF <formula><tex>$=$</tex></formula> 83<formula><tex>$\%$</tex></formula>) and emergency (VAF <formula><tex>$=$</tex></formula> 65<formula><tex>$\%$</tex></formula>) scenarios. The promising results show that including signal-dependent noise and modeling steering as a reaching task are steps in the right direction in the field of driver modeling. The model, however, poorly captured the lateral deviation behavior, primarily suspected due to the satisficing behavior exhibited by humans. Developing a nonlinear-iterative version of the IDV model could address the limitations.