  This study presents a novel approach for modeling and simulating
human-vehicle interactions in order to examine the effects of automated driving
systems (ADS) on driving performance and driver control workload. Existing
driver-ADS interaction studies have relied on simulated or real-world human
driver experiments that are limited in providing objective evaluation of the
dynamic interactions and control workloads on the driver. Our approach
leverages an integrated human model-based active driving system (HuMADS) to
simulate the dynamic interaction between the driver model and the haptic-based
ADS during a vehicle overtaking task. Two driver arm-steering models were
developed for both tense and relaxed human driver conditions and validated
against experimental data. We conducted a simulation study to evaluate the
effects of three different haptic shared control conditions (based on the
presence and type of control conflict) on overtaking task performance and
driver workloads. We found that No Conflict shared control scenarios result in
improved driving performance and reduced control workloads, while Conflict
scenarios result in unsafe maneuvers and increased workloads. These findings,
which are consistent with experimental studies, demonstrate the potential for
our approach to improving future ADS design for safer driver assistance
systems.
