Modeling Lateral Control Behaviors of Distracted Drivers for Haptic-Shared Steering System

Haptic-shared steering (HSS) systems have been reported to enhance vehicle safety and reduce the workload for drivers. However, few studies have focused on modeling the lateral vehicle control behaviors of distracted drivers for HSS. The current study models this type of behavior in a series of high-fidelity driving simulator experiments with 18 participants. Two experimental conditions for a double lane change task are tested: HGT-Constant (haptic guidance torque with a constant gain) and HGT-Adaptive (haptic guidance torque with an adaptive gain). A gated recurrent unit (GRU) network is used to model lateral control behavior during driving. The effectiveness of the GRU network-based lateral control model of distracted drivers is benchmarked using a state-of-the-art long short-term memory network, a back propagation network, an extreme learning machine, and a traditional two-point visual model with neuromuscular dynamics. Experimental results indicate that the GRU network has the highest accuracy in terms of root mean square error, mean absolute error, mean absolute percentage error, and determination coefficient. In addition, a simulation model of the driver-vehicle-road closed-loop system demonstrates that the proposed model predicts driver behavior with acceptable lateral position error.