Model-based Evaluation of Driver Control Workloads in Haptic-based Driver Assistance Systems

TL;DR

This paper introduces a model-based simulation approach to evaluate driver control workloads in haptic-assisted automated driving, providing insights into how different control conflicts affect safety and performance.

Contribution

It develops an integrated human model-based system to simulate driver-ADS interactions, validating models against experimental data and analyzing control workload impacts.

Findings

No Conflict control improves performance and reduces workload.

Conflict scenarios lead to unsafe maneuvers and higher workloads.

Simulation results align with experimental observations.

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.