  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.

Human-Computer Interaction;Robotics;Systems and Control;Systems and Control