ITSC 2025 Paper Abstract

Paper TH-LM-T22.5

Kim, Jaeyoon (University of Texas at Austin), Wang, Junmin (The University of Texas at Austin)

A Personalizable and Physics-Based Model Quantifying Expected Driver Attentiveness in Vehicle Lane-Keeping Automation

Scheduled for presentation during the Invited Session "S22a-Emerging Trends in AV Research" (TH-LM-T22), Thursday, November 20, 2025, 11:50−12:10, Coolangata 1

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Keywords Human-Machine Interaction Systems for Enhanced Driver Assistance and Safety, Trust, Acceptance, and Public Perception of Autonomous Transportation Technologies, Driver Behavior Monitoring and Feedback Systems for Semi-autonomous Vehicles

Abstract

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.