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Shared steering control has been developed to reduce driver workload whilekeeping the driver in the control loop. A driver could integrate visual sensoryinformation from the road ahead and haptic sensory information from thesteering wheel to achieve better driving performance. Previous studies suggestthat, compared with adaptive automation authority, fixed automation authorityis not always appropriate with respect to human factors. This paper focuses ondesigning an adaptive shared steering control system via sEMG (surfaceelectromyography) measurement from the forearm of the driver, and evaluates theeffect of the system on driver behavior during a double lane change task. Theshared steering control was achieved through a haptic guidance system whichprovided active assistance torque on the steering wheel. Ten subjectsparticipated in a high-fidelity driving simulator experiment. Two types ofadaptive algorithms were investigated: haptic guidance decreases when drivergrip strength increases (HG-Decrease), and haptic guidance increases whendriver grip strength increases (HG-Increase). These two algorithms werecompared to manual driving and two levels of fixed authority haptic guidance,for a total of five experimental conditions. Evaluation of the driving systemswas based on two sets of dependent variables: objective measures of driverbehavior and subjective measures of driver workload. The results indicate thatthe adaptive authority of HG-Decrease yielded lower driver workload and reducedthe lane departure risk compared to manual driving and fixed authority hapticguidance.

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