Modeling Lateral Control Behavior of Driver for Manual Takeover of Automated Vehicles During Pedestrian Collision Avoidance

Due to complex and uncertain driver behaviors during pedestrian collision avoidance (PCA) maneuvers, it is challenging to accurately describe human driver behaviors for manual takeover of automated vehicles. In this context, this article proposes a novel modeling method integrating neuromuscular dynamics with a long short-term memory (LSTM) network. A neuromuscular dynamics model is responsible for capturing the dynamic interaction between the steering system and the neuromuscular system of the driver, whereas a LSTM network is used to describe the visual behaviors of the driver accurately during manual takeover. A series of high-fidelity driving simulator experiments with 12 participants were conducted to acquire driver behaviors during manual takeover under PCA conditions. Scenarios with manual takeover steering and conventional manual steering were compared to analyze driver behaviors under PCA maneuvers. In terms of root mean square error, mean absolute error, and determination coefficient, the experimental results indicated that the proposed method had higher identification accuracy than a method combining two-point visual modeling and neuromuscular dynamics. The proposed method also captured the dynamic interaction between the vehicular steering system and the neuromuscular system. Thus, in the interest of developing manual takeover control systems of automated vehicles, the proposed method can better represent lateral behaviors of drivers.