Adaptive driver-automation shared steering control via forearm surface electromyography measurement

TL;DR

This study develops an adaptive shared steering control system using forearm surface electromyography to adjust assistance based on driver grip strength, improving driver workload and safety during lane change tasks.

Contribution

It introduces a novel adaptive algorithm for shared steering control that adjusts assistance based on real-time electromyography signals, enhancing human factors in automation.

Findings

Adaptive authority reduces driver workload.

Adaptive system decreases lane departure risk.

Compared to fixed guidance and manual driving.

Abstract

Shared steering control has been developed to reduce driver workload while keeping the driver in the control loop. A driver could integrate visual sensory information from the road ahead and haptic sensory information from the steering wheel to achieve better driving performance. Previous studies suggest that, compared with adaptive automation authority, fixed automation authority is not always appropriate with respect to human factors. This paper focuses on designing an adaptive shared steering control system via sEMG (surface electromyography) measurement from the forearm of the driver, and evaluates the effect of the system on driver behavior during a double lane change task. The shared steering control was achieved through a haptic guidance system which provided active assistance torque on the steering wheel. Ten subjects participated in a high-fidelity driving simulator experiment. Two types of adaptive algorithms were investigated: haptic guidance decreases when driver grip strength increases (HG-Decrease), and haptic guidance increases when driver grip strength increases (HG-Increase). These two algorithms were compared to manual driving and two levels of fixed authority haptic guidance, for a total of five experimental conditions. Evaluation of the driving systems was based on two sets of dependent variables: objective measures of driver behavior and subjective measures of driver workload. The results indicate that the adaptive authority of HG-Decrease yielded lower driver workload and reduced the lane departure risk compared to manual driving and fixed authority haptic guidance.