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Open research questions in Human-Automation Interaction and Safety

418 gap statements mined from Human-Automation Interaction and Safety papers in our 7.9M-paper local library, which holds 2,588 papers on the topic — drawn mostly from each paper's own stated research gap, future-work, challenge and limitation notes, and its abstract. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one.

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What the literature leaves open

  • The lack of environmental features in laboratory experiments can limit the validity of the results.

    Exploring Female Volunteer Head and Thorax Kinematics During a Braking Pulse in the Presence of a Steering Wheel · 2026 · DOI

  • The study identifies the challenge of understanding how virtual models influence users' psychological needs. The study recognizes the challenge of designing AI interaction interfaces that adapt to user needs. The study acknowledges the challenge of promoting the formation of user driving habits and continuous use intention in autonomous driving systems.

    Mechanisms of a Self-Determination Theory-Driven Human–AI Co-Driving Model: Effects on Driving Habits and Continued Usage Intention · 2026 · DOI

  • Prior research has focused on trust and intention to use, with less attention to motivational and system-feature factors. There is a gap in understanding how virtual models influence users' psychological needs. The study aims to address this gap by constructing an extended framework for human-AI co-driving behavior based on SDT.

    Mechanisms of a Self-Determination Theory-Driven Human–AI Co-Driving Model: Effects on Driving Habits and Continued Usage Intention · 2026 · DOI

  • The lack of transparency in the sequential decision-making processes of generative AI. The inability to evaluate output reliability in high-risk settings.

    Architectural Gaps in Generative AI: Quantifying Cognitive Risks for Safety Applications · 2025 · DOI

  • Little is known about how to address the concerns of adolescents, parents, and school bus drivers or how to translate them into interaction design.

    Shaping Future Automated School Bus Systems: Multi-Stakeholder Insights Informing Human-in-the-Loop Design · 2026 · DOI

  • Large language models (LLMs) and assistive tools that capitalise on LLMs are becoming an integral part of work, yet their impact on experiences during daily work tasks remains underexplored.

    Working with Large Language Models: impacts on performance, efficiency and perceived responsibility · 2026 · DOI

  • However, to date, little is known about drivers’ needs for BVR and implicit information in CF events.

    From brake lights to beyond – exploring information needed for car-following behaviour · 2026 · DOI

  • While previous research primarily studied AS with safety-critical considerations, little is known about how users encounter and respond to such surprises during non-critical automated driving.

    Experience-centred lens on automation surprise in non-critical automated driving: findings from an on-road WoZ study · 2026 · DOI

  • The study suggests directions for further investigation into improving trust assessment techniques and developing practical models to better suit the evolving context of human-autonomy collaboration.

    A Recent Review on Subjective and Objective Assessment of Trust in Human Autonomy Teaming · 2025 · DOI

  • While AV explanations with different information types have been studied to enhance drivers' trust and SA, their effectiveness remains unclear when AVs make errors that do not trigger takeover requests.

    Watch Out for Explanations: Information Type and Error Type Affect Trust and Situational Awareness in Automated Vehicles · 2025 · DOI

  • While previous research has established the benefits of external human–machine interfaces (HMIs) for communicating AV intent, this study extended this knowledge by focusing on the vital but underexplored interaction with CV drivers.

    Investigating HMIs to Foster Communications Between Conventional Vehicles and Autonomous Vehicles at Intersections · 2025 · DOI

  • These efforts were successful, but the validation studies used artificial examples with limited data.

    Validation of a Formal Method for Human Error Rate Prediction With Negative Transfer · 2025 · DOI

  • Although AI is increasingly assuming autonomous managerial roles within organisations, its leadership effectiveness relative to that of human managers remains lacking.

    Hybrid human–AI leadership: exploring the influence of leadership structure on leadership effectiveness and neural activation · 2025 · DOI

  • Existing research indicates that most drilling incidents are related to human factors, but the influence of mental workload on drillers’ risk perception in well control remains underexplored.

    Evaluating the impact of mental workload on drillers’ risk perception using wearable eye-tracking technology · 2025 · DOI

  • Despite its importance, little is known about the implications of automaticity in the construction industry.

    Application of Automaticity Theory in Construction · 2024 · DOI

  • Specifically, recent developments in ADAS have made more advanced functions available but they have been under-investigated.

    Exploring Factors Related to Drivers’ Mental Model of and Trust in Advanced Driver Assistance Systems Using an ABN-Based Mixed Approach · 2024 · DOI

  • Trust is crucial for ensuring the safety, security, and widespread adoption of automated vehicles (AVs), and if trust is lacking, drivers and the general public may hesitate to embrace this technology.

    Building Contextualized Trust Profiles in Conditionally Automated Driving · 2024 · DOI

  • Our results imply that alternatives to the common ‘recommend-and-explain’ approach should be explored to assist human decision-making in challenging tasks.

    Measuring the effect of mental workload and explanations on appropriate AI reliance using EEG · 2024 · DOI

  • It often remains unclear when people can rely appropriately on AI and when explanations can help.

    Measuring the effect of mental workload and explanations on appropriate AI reliance using EEG · 2024 · DOI

  • Studies have explored this reluctance, but little is known about how different people use algorithms.

    Are engineers more likely to avoid algorithms after they see them err? A longitudinal study · 2024 · DOI

  • However, whether other forms of eHMI, such as text-based, anthropomorphized, or symbolic eHMI, can improve the suboptimal performance of abstract eHMI at partial penetration condition remains to be further investigated. Therefore, the results of this study need to be validated in future real-world studies.

    Neuroergonomic evaluation of risk-warning eHMI penetration rates in vehicle platoons: effects on pedestrians’ mental workload, situation awareness, and gap acceptance · 2026 · DOI

  • The sample size is limited to 116 knowledge workers. The study does not validate the model across industries.

    Artificial intelligence system reliability and knowledge identity: A model for knowledge workers in knowledge management environments · 2026 · DOI

  • Limited understanding of how AI influences the human and identity-related dimensions of organisational knowledge. Prior work on AI trustworthiness has largely emphasised technical performance.

    Artificial intelligence system reliability and knowledge identity: A model for knowledge workers in knowledge management environments · 2026 · DOI

  • Investigating the influence of stronger braking pulses on kinematic and muscular responses, - Examining the differences in kinematic reactions between male and female participants, - Studying the effects of different steering wheel positions on participant behavior, - Analyzing the impact of autonomous driving on driver behavior…

    Exploring Female Volunteer Head and Thorax Kinematics During a Braking Pulse in the Presence of a Steering Wheel · 2026 · DOI

  • Existing approaches are still scattered across behavioral tasks and encounter issues such as real-time processing, privacy preservation, scalability, model generalization, and integration of heterogeneous data, - The need for integrated and task-oriented approaches to address these challenges and support multiple behavioral assessments, - The lack of research on the development of scalable privacy-aware real-time…

    Vehicular driving pattern analysis: concepts, challenges, and future directions · 2026 · DOI

Most-cited papers in Human-Automation Interaction and Safety

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Open questions raised by papers listed on this page include: (1) The lack of environmental features in laboratory experiments can limit the validity of the results (“Exploring Female Volunteer Head and Thorax Kinematics During a Braking Pulse in…”, 2026); (2) Little is known about how to address the concerns of adolescents, parents, and school bus drivers or how to translate them into interaction design (“Shaping Future Automated School Bus Systems: Multi-Stakeholder Insights…”, 2026); (3) Large language models (LLMs) and assistive tools that capitalise on LLMs are becoming an integral part of work, yet their impact on experiences during daily work tasks remains underexplored (“Working with Large Language Models: impacts on performance, efficiency and…”, 2026); (4) To date, little is known about drivers’ needs for BVR and implicit information in CF events (“From brake lights to beyond – exploring information needed for car-following…”, 2026); (5) While previous research primarily studied AS with safety-critical considerations, little is known about how users encounter and respond to such surprises during non-critical automated driving (“Experience-centred lens on automation surprise in non-critical automated…”, 2026). 418 gap statements have been mined from Human-Automation Interaction and Safety papers in our 7.9M-paper local library, which holds 2,588 papers on the topic; the gaps come from whichever of those papers state one. They are mostly the research gaps the authors state and the papers' abstracts, plus future-work, limitations and challenges passages. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one, so you can read the original claim in context.

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