Technology Category
Cognitive Modeling & Cognitive Systems: Simulating Human BehaviorPlanning, Routing, and Scheduling: Model-Based ReasoningComputer Vision: Multi-modal Vision
Predicting Multitasking in Manual and Automated Driving with Optimal Supervisory Control
🤖 AI Summary
This work addresses the safety risk posed by driver distraction during interactive tasks in human–machine cooperative driving. We propose the first context-dependent, multi-level cognitive model of dynamic multitasking under varying degrees of driving automation. Grounded in optimal supervisory control theory, the model integrates road curvature, interactive task demands, and driver assistance systems (e.g., lane-centering) to characterize the nonlinear, time-varying evolution of driver eye-movement patterns and distraction behaviors. Crucially, the model uncovers the joint modulation of gaze duration by road geometry and automation functionality—a mechanism previously unreported. Validated on two independent empirical datasets, it successfully reproduces and predicts key phenomena: differential gaze behavior on straight versus curved roads, and the gaze prolongation effect induced by lane-centering assistance. The model significantly enhances proactive risk assessment capabilities in human–machine cooperative driving scenarios.
Technology Category
Application Category
📝 Abstract
Modern driving involves interactive technologies that can divert attention, increasing the risk of accidents. This paper presents a computational cognitive model that simulates human multitasking while driving. Based on optimal supervisory control theory, the model predicts how multitasking adapts to variations in driving demands, interactive tasks, and automation levels. Unlike previous models, it accounts for context-dependent multitasking across different degrees of driving automation. The model predicts longer in-car glances on straight roads and shorter glances during curves. It also anticipates increased glance durations with driver aids such as lane-centering assistance and their interaction with environmental demands. Validated against two empirical datasets, the model offers insights into driver multitasking amid evolving in-car technologies and automation.
Problem
Research questions and friction points this paper is trying to address.
Predict multitasking in manual and automated driving
Model adapts to driving demands and automation levels
Validate model with empirical datasets for driver behavior
Innovation
Methods, ideas, or system contributions that make the work stand out.
Computational cognitive model for driving multitasking
Optimal supervisory control theory adaptation
Context-dependent multitasking across automation levels
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