Driver’s Mental Models of Advanced Vehicle Technologies: A Proposed Framework for Identifying and Predicting Operator Errors

Pradhan, Anuj K.; Pai, Ganesh; Knodler, Michael A.; Fitzpatrick, Cole; Horrey, William J · 2021 · ROSA P / Safety Research Using Simulation (SAFER-SIM) University Transportation Center

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Summary

This paper addresses the critical challenge of characterizing drivers’ mental models of Advanced Driver Assistance Systems (ADAS), specifically Adaptive Cruise Control (ACC). As vehicle automation increases, drivers must understand system capabilities and limitations to ensure safe operation. However, existing research indicates significant gaps in driver knowledge, leading to errors such as mode confusion, inappropriate reliance, and "out-of-the-loop" phenomena. The authors argue that because mental models are difficult to quantify directly, a framework for identifying and predicting operator errors can serve as a proxy for assessing the quality and accuracy of these mental models. The study aims to elucidate ACC from a user-control perspective and develop tools to map potential error types to specific aspects of driver-system interactions. The methodology combines a review of scientific literature on mental models and human error taxonomies with a detailed task analysis of ACC systems. The researchers constructed state diagrams to visualize ACC as a finite state machine, defining five distinct states (e.g., off, on but unengaged, engaged with/without a lead vehicle, temporarily disengaged) and the transitions between them. These diagrams were built using manufacturer documentation, including owner’s manuals and promotional materials, for a generic ACC system and five specific mass-market vehicles (Ford F-150, Toyota RAV4, Volvo XC60, Honda CR-V, and Subaru Outback). Additionally, the study analyzed manufacturer reporting of system limitations to assess the clarity and detail of available information. The core contribution is a proposed framework that leverages task analysis techniques, such as Systematic Human Error Reduction and Prediction Approach (SHERPA), to identify potential errors during state transitions and classify them using established taxonomies (e.g., Reason’s slips, lapses, mistakes, and violations; Rasmussen’s skill, rule, and knowledge errors). The findings highlight the inherent complexity of ACC systems, which varies across manufacturers. While commonalities exist, differences in intermediate states and transition triggers—such as Volvo’s Passing Assistance—demonstrate that system complexity is not uniform. The state diagrams reveal that ACC requires continuous driver monitoring and specific inputs to manage state changes, underscoring the risk of errors when drivers lack accurate mental models. The analysis of manufacturer materials showed variability in how system limitations are reported, suggesting that inconsistent or insufficient documentation may contribute to drivers’ incomplete mental models. By mapping tasks to error types, the framework identifies how misunderstandings of operational design domains or system modes can lead to specific errors, such as engaging the system in unsupported conditions or failing to recognize when the system has disengaged. The significance of this work lies in providing a structured method for researchers and designers to evaluate driver-automation interactions. By linking operator errors to specific gaps in mental models, the framework offers a pathway to assess the safety implications of ADAS deployment. It suggests that improving the clarity of system states and transitions, as well as enhancing the quality of instructional materials, can help align drivers’ mental models with system realities. This approach supports the development of better training, interface design, and evaluation scenarios for advanced vehicle technologies, ultimately aiming to reduce error rates and improve safety as automation levels increase.

Key finding

The study presents a proposed framework utilizing state diagrams and task analysis to identify and predict operator errors by mapping them to gaps in drivers' mental models of ACC systems.

Methodology

review

Provenance

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Topics

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• situational awareness
• human error taxonomy
• mode awareness
• automation surprise
• mental model of traffic
• pre crash contributing factors

Information type

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• Theoretical Contribution: conceptual framework, computational model, theory or model