Model-based Evaluation of Driver Control Workloads in Haptic-based Driver Assistance Systems
URL: http://arxiv.org/abs/2210.13609v1
archive: archived pipeline: in_review verified
Abstract
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.
Summary
Simulation study using the HuMADS (human model-based active driving system, OpenSim + SimBody) to evaluate driver control workload and force-torque interactions across three haptic shared-control (HSC) conditions during a Level-2 vehicle overtaking task: No Conflict (driver and ADS aligned), Conflict-I (opposite directions), and Conflict-II (same direction, different trajectories). Two arm-steering driver models (tense and relaxed) were tuned and validated against published human-driver experimental data (Pick & Cole 2007). Driver workload was decomposed into control stress, load quantity, and joint-torque/reaction-force at the right hand and shoulder; ADS torque saturation at 5 Nm was tracked across conditions.
Key finding
No-Conflict haptic shared control (relaxed driver) produced the lowest driver steering torque (1.04 Nm vs 2.47 Nm in manual control), lowest peak hand reaction force (4.2 N vs 30.1 N for Conflict-I), and lowest control stress/load quantity, while Conflict-I conditions saturated the ADS torque limit and produced the highest workload, confirming that aligned haptic guidance reduces driver effort whereas conflicting guidance increases it.
Methodology
simulation_modeling
Sample size: N/A (model-based simulation; driver models validated against Pick & Cole 2007 experimental human-subject steering data; vehicle inputs from Naranjo et al. 2008)