The Dynamics of Attention across Automated and Manual Driving Modes: A Driving Simulation Study

2026-02-04

AI Summary

This study addresses the risk of unsafe takeovers during transitions between driving modes in automated vehicles, which arises from unclear attention allocation by drivers. Combining high-fidelity driving simulation with eye-tracking technology, the research employs generalized linear mixed models (GLMMs) to analyze drivers’ gaze dynamics toward critical areas—including the road, rearview mirror, human–machine interface (HMI), and speedometer—across manual, automated, and transitional phases. Results reveal that during automated driving, drivers significantly increase their fixation duration on the HMI, while during takeover transitions, their attention rapidly alternates between the external environment and the interface. These findings demonstrate marked differences in visual attention allocation across driving modes, offering empirical support for the design of context-aware adaptive HMIs and targeted driver training strategies to enhance transition safety.

Technology Category

• Application Domains: Mobility, Driving & Flight
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Computer Vision: Multi-modal Vision

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

This study aims to explore the dynamics of driver attention to various zones, including the road, the central mirror, the embedded Human-Machine Interface (HMI), and the speedometer, across different driving modes in AVs. The integration of autonomous vehicles (AVs) into transportation systems has introduced critical safety concerns, particularly regarding driver re-engagement during mode transitions. Past accidents underscore the risks of overreliance on automation and highlight the need to understand dynamic attention allocation to support safety in autonomous driving. A high-fidelity driving simulation was conducted. Eye-tracking technology was used to measure fixation duration, fixation count, and time to first fixation across distinct driving modes (automated, manual, and transition), which were then used to assess how drivers allocated attention to various areas of interest (AOIs). Findings show that drivers'attention varies significantly across driving modes. In manual mode, attention consistently focuses on the road, while in automated mode, prolonged fixation on the embedded HMI was observed. During the handover and takeover phases, attention shifts dynamically between environmental and technological elements. The study reveals that driver attention allocation is mode-dependent. These findings inform the design of adaptive HMIs in AVs that align with drivers'attention patterns. By presenting relevant information according to the driving context, such systems can enhance driver-vehicle interaction, support effective transitions, and improve overall safety. Systematic analysis of visual attention dynamics across driving modes is gaining prominence, as it informs adaptive HMI designs and driver readiness interventions. The GLMM findings can be directly applied to the design of adaptive HMIs or driver training programs to enhance attention and improve safety.

Problem

• driver attention
• autonomous vehicles
• mode transition
• human-machine interface
• driving safety

Innovation

• eye-tracking
• adaptive HMI
• mode transition
• visual attention dynamics
• autonomous driving