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Open research questions in Human-Automation Interaction and Safety

177 unresolved questions extracted from the limitations and future-work sections of 1,162 Human-Automation Interaction and Safety papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • Substantial inaccuracies in joint angles and risk estimates were found. The system had concerns about reliability, particularly in cluttered environments and tasks involving fine hand motions. The system may require further refinements to improve accuracy and adaptability in real-world settings.

    Evaluating the accuracy and feasibility of a commercial AI-powered ergonomic assessment system for automotive assembly work · 2026 · DOI

  • The sample size in study 1 was relatively small due to the novel and exploratory nature of the work and limited prior data to support a priori power estimation. The simulated tasks were limited in scope, with only one trial per task type and no assessment of more complex movements such as overhead assembly, confined-space tasks, or repetitive fine-motor tasks with substantially deviated wrist postures. The findings of this study may not generalize to the wide range of dynamic postures and movements observed in real industrial settings.

    Evaluating the accuracy and feasibility of a commercial AI-powered ergonomic assessment system for automotive assembly work · 2026 · DOI

  • The study identifies the challenge of minimizing participants' need for conjecture about the largely unknown future SAV service. The study also identifies the challenge of creating a concise measure of the MAVA.

    Exploring the general acceptance factor for shared automated vehicles: the impact of personality traits and experimentally altered information · 2025 · DOI

  • improve the understanding of SAV acceptance to harvest the full potential benefits, - investigate the impact of other personality traits on SAV acceptance, - explore the effects of different informational conditions on SAV acceptance

    Exploring the general acceptance factor for shared automated vehicles: the impact of personality traits and experimentally altered information · 2025 · DOI

  • Improper design of AR-HUD driving warning interfaces can increase cognitive load and affect safety. The complexity of the traffic environment makes driving safety an urgent issue. The need to provide enhanced driving safety information that matches the actual environment is a challenge.

    Design and Evaluation of Ecological Interface of Driving Warning System Based on AR-HUD · 2024 · DOI

  • Although cognitive workload measurement has been explored in controlled settings, the ability of TSFMs to generalize to unseen individuals for CWL classification from eye-tracking data has not been studied.

    Time-Series Foundation Models for Cognitive Workload Classification using Eye-Tracking Data · 2026 · DOI

  • BackgroundAs automated vehicles (AV) expand their operational design domain, little is known about driver interactions with driving automation in complex urban settings.

    With a Little Help From My Car: Sharing Automated Vehicle’s Situation Awareness Reduces Driver-Initiated Automation Disengagement Without Delaying Takeover Response Time · 2026 · DOI

  • However, little is known about how prior expectations and new information about target prevalence interact in simulated emergency scenarios.

    Self-Generated Expectations of Hazard Prevalence Affect Virtual Search and Rescue · 2026 · DOI

  • ObjectiveExplore the under-investigated display attribute of head-mounted display (HMD) opacity and its relationship with display clutter and environment clutter in real-world nautical navigation tasks.

    Effects of Environmental Clutter, Head-Mounted Display Clutter, and Head-Mounted Display Transparency on Human Efficiency, Safety, and Mental Demand During Navigation · 2026 · DOI

  • BackgroundWhile artificial intelligence (AI) can enhance decision-making efficiency and accuracy in emergencies, the mechanisms underlying human use of AI in such contexts remain poorly understood.

    Human–Artificial Intelligence Collaborative Decision-Making in Emergencies: Relative Advantage Theory · 2026 · DOI

  • Background XAI aims to improve understanding, calibrate trust, and enhance performance of an HAT, but the impact of explanation type in realistic, high-taskload HAT settings remains underexplored.

    Comprehensive Evaluation of Explanation Types in a Spaceflight-Relevant Human–Autonomy Teaming Task · 2026 · DOI

  • BackgroundThe Strategic Task Overload Management (STOM) model posits that task priority and difficulty influence task-switching behavior, but empirical research has yielded inconsistent results.

    Effects of Task Priority and Difficulty in Multitasking Across Screens · 2026 · DOI

  • ResultsCommunication inflexibility, limited shared understanding, and trust miscalibration emerge as recurring barriers to HAT, while regulatory capacities (trust calibration and metacognitive awareness) represent particularly critical dimensions of HAT readiness that remain to be fully operationalized.

    Cognitive Readiness for Human-AI Collaboration · 2026 · DOI

  • BackgroundAlthough cognitive load affects trust in automation, its influence on the mechanisms of trial-by-trial trust updating remains unclear.

    How Cognitive Load Affects Dynamic Trust Calibration in Human–AI Collaboration: Evidence for Selective Pathway Effects · 2026 · DOI

  • However, little is known about how driver experience shapes responses to workload demands in underground coal mine transport operations.

    Cognition: Effects of Cognitive Workload on Driving Performance: A Comparison of Novice and Experienced Drivers in Underground Coal Mine Transport Vehicles · 2026 · DOI

  • However, prior studies across domains have shown mixed results regarding whether structured scanning approaches enhance performance.

    From Protocol to Structured Gaze Patterns: The Effectiveness of Systematic Viewing in Remote Nautical Object Control · 2026 · DOI

  • Prior research has established that time pressure and low automation reliability impair monitoring performance, but the underlying cognitive mechanisms remain unclear.

    Automation Reliability Impairs Evidence Accumulation Efficiency: Computational Modeling of Monitoring Under Time Pressure · 2026 · DOI

  • While industry practice emphasizes high instrumentality, entertainment value, and anthropomorphic features, whether these design attributes reduce users' psychological burden and promote collaboration willingness in cognitively demanding driving tasks remains unclear.

    Partner or burden? The dual pathways linking perceived attributes of intelligent cockpits to human–machine collaboration willingness via cognitive load · 2026 · DOI

  • Startle and surprise can occur either together or independently, yet no studies have experimentally distinguished their specific effects.

    Investigating the Independent and Combined Effects of Startle and Surprise in a Simulated Flight Task · 2025 · DOI

  • XAI offers opportunities to support decision making by providing insights into AI's reasoning, yet its adoption and effectiveness in multitasking scenarios remain underexplored.

    The Effect of Workload and Task Priority on Multitasking Performance and Reliance on Level 1 Explainable AI (XAI) Use · 2025 · DOI

  • There were mixed results regarding awareness of the location of objects and events outside of the assembly task.

    Cognitive Aid Design Using Diminished Reality to Support Selective Attention by Reducing Distraction · 2025 · DOI

  • BackgroundLimited research has examined the extent to which within-person variability in operator states predicts RTM performance, a prerequisite to adapting work systems based on expected performance degradation/operator strain.

    Predicting Return-to-Manual Performance in Lower- and Higher-Degree Automation · 2025 · DOI

  • This creates a knowledge gap in understanding shared perception strategies for piloting in environments with impaired sensory channels or enhanced secondary cues.

    Multimodal Cueing in Attitude Tracking: Predicting Pilot Mental Workload Through Physiological Measurements · 2025 · DOI

  • However, few studies have investigated the driver-touchscreen interaction during automated driving.

    Effects of In-Vehicle Touchscreen Location on Driver Task Performance, Eye Gaze Behavior, and Workload During Conditionally Automated Driving: Nondriving-Related Task and Take-Over · 2024 · DOI

  • Previous studies presented mixed results regarding the impact of displaying likelihood information and explanations, and often relied on hand-created information, limiting scalability and failing to address real-world dynamics.

    More Is Not Always Better: Impacts of AI-Generated Confidence and Explanations in Human–Automation Interaction · 2024 · DOI

Most-cited papers in Human-Automation Interaction and Safety

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177 open questions have been extracted from the limitations and future-work passages of 1,162 Human-Automation Interaction and Safety papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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