Junmin Wang

This paper presents a model predictive control (MPC) approach for personalized lane-keeping automation that tracks driver-specific reference trajectories. Human subjects repeated runs across diverse driving conditions were conducted on a driving simulator. For each subject, the driving data are summarized into a representative trajectory that serves as the control reference and shows high-order shapes. The prediction model employs the Frenet-error dynamics and uses preview reference road curvature as a measured disturbance, ensuring consistent use of preview information. This control approach reduces phase lag and overshoot while consistently optimizing costs within constraints over the prediction horizon. In a dSPACE Automotive Simulation Models (ASM) environment, the proposed controller outperforms its baseline controller by improving tracking accuracy for multiple drivers in various driving scenarios, demonstrating the feasibility of personalized path tracking without additional feedforward loops and the practicality of the proposed framework.

This paper presents a personalizable, physics-based model that quantifies individual drivers' expected attentiveness under varying driving conditions in vehicle lane-keeping automation. The model introduces a physically interpretable formulation for drivers' cognitive load as a function of vehicle speed and road curvature. It leverages intuitive, personalized indicators derived from gaze data, offering greater interpretability than conventional gaze metrics and enabling driver-specific customization. Using a high-fidelity driving simulator and an eye-tracking system, we collected objective gaze data and applied a hybrid method combining subjective ratings with the NASA Task Load Index to optimize model parameters. We evaluated the model's predictive performance and reliability with human subject experiments from multiple perspectives. This model supports human-centric vehicle automation by estimating if a driver is under-, over-, or appropriately attentive.

Repeated driving along the same route is common in the real world and promotes familiarity, which significantly influences driving behavior and safety. While prior studies have characterized familiarity using aggregated route-level trends, little attention has been given to how it manifests across diverse scenarios and individuals. This study adopts a counterexample-driven approach to examine whether such global summaries adequately reflect nuanced behavioral adaptations across contexts and drivers. A preliminary study was conducted using a driving simulator with eye tracking. Participants repeatedly drove a virtual urban route containing intersections, curves, and regulatory signs. Gaze behavior was analyzed using stationary and dynamic metrics to capture changes in visual attention. Results show that while global route-level trends indicate increased driving speed and decreased gaze entropy, scenario- and individual-level analyses reveal substantial variability. Notably, the common assumption that fixation duration on traffic signs uniformly decreases with increasing familiarity is challenged, as one subject continued using stop signs as visual references for stopping. These findings highlight the limitations of relying solely on global analyses and underscore the value of incorporating scenario- and individual-level perspectives to better understand how driving familiarity develops. This shift may inform the design of adaptive, human-centric autonomous systems with improved safety and efficiency.