Familiarity and Complexity during a Takeover in Highly Automated Driving

Scharfe-Scherf, Marlene Susanne Lisa; Russwinkel, Nele · 2021 · Crossref

DOI: 10.1007/s13177-021-00259-0

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Summary

This study investigates how situation familiarity and objective traffic complexity influence subjective complexity and the time required to make an action decision during takeover requests in highly automated driving. As Level 3 automation requires drivers to serve as fallback operators, understanding the cognitive processes underlying the transition from automated to manual control is critical for safety. The research aims to determine if objective environmental factors and driver familiarity with specific traffic scenarios predict the driver’s perceived complexity and subsequent decision-making speed, thereby informing the development of individualized cognitive assistance systems. The researchers conducted an experimental laboratory study using a high-fidelity driving simulator with a 360-degree view. Twenty participants engaged in 18 trials across three blocks, each containing six distinct traffic scenarios. The independent variables were objective complexity, manipulated by varying the number of relevant vehicles in the traffic environment (ranging from zero to six vehicles), and situation familiarity, manipulated by repeating each scenario three times. Participants performed a non-driving-related task on a tablet during automated driving phases. Upon receiving a takeover request, they were instructed to verbally indicate their intended maneuver as soon as the decision was made. Subjective complexity was measured immediately after each trial using the NASA Task Load Index questionnaire. The results demonstrated that both situation familiarity and objective complexity significantly influenced subjective complexity and the time to make an action decision. Specifically, higher familiarity with a traffic situation led to a decrease in perceived subjective complexity and shorter action decision times. Conversely, higher objective complexity, defined by a greater number of relevant vehicles, increased subjective complexity and prolonged the time required to reach a decision. Crucially, the study found that subjective complexity acts as a mediator variable between the independent variables (familiarity and objective complexity) and the dependent variable (time to action decision). This indicates that the impact of environmental factors and familiarity on decision speed is not direct but is filtered through the driver’s subjective perception of the situation's complexity. These findings highlight the importance of considering both objective traffic conditions and driver familiarity when designing takeover systems for highly automated vehicles. The identified mediation effect suggests that cognitive assistance systems could be improved by assessing a driver’s subjective complexity in real-time. By modeling these cognitive states, systems could adapt their support strategies to individual drivers, potentially reducing takeover times and enhancing safety. The study provides a foundational basis for implementing cognitive modeling in automated driving interfaces to facilitate smoother and safer transitions of control.

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Topics

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• situational awareness
• road complexity
• automation surprise
• automation
• mental demand
• takeover transitions

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• Empirical Findings: behavioral performance data
• Theoretical Contribution: conceptual framework, theory or model