Beyond adaptive cruise control and lane centering control: drivers' mental model of and trust in emergency lane keeping

Huang, Chunxi; He, Dengbo; Wen, Xiao; Yan, Song · 2023 · Frontiers in Psychology

DOI: 10.3389/fpsyg.2023.1236062

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Summary

This study addresses the gap in understanding drivers’ mental models of and trust in emerging Advanced Driver Assistance Systems (ADAS) that go beyond traditional Adaptive Cruise Control (ACC) and Lane Centering Control (LCC). While previous research focused on traditional Level-2 ADAS, the rapid adoption of "advanced Level 2" systems (e.g., Tesla’s Navigate on Autopilot) in China, where 32.4% of vehicles sold in early 2022 included ADAS, necessitates an investigation into how Chinese drivers perceive these complex systems. The study aims to identify factors influencing drivers’ knowledge of ADAS functions and limitations and how these mental models correlate with trust, thereby informing driver education and safety design. The research employed a survey design targeting 287 valid responses from ADAS users in the Chinese market. The questionnaire assessed demographics, driving experience, and specific ADAS knowledge through 49 statements covering ACC, LCC, emerging functions (such as automated lane changing and stop-and-go), and system limitations. Participants rated their agreement with these statements on a 6-point scale. Additionally, trust in ADAS was measured using a five-item scale. The study utilized cluster analysis to group drivers based on their knowledge profiles and regression models to identify predictors of these clusters and trust levels. Results indicated that drivers generally possessed weak knowledge of LCC and emerging ADAS functions. Only 9% of respondents demonstrated a relatively strong mental model of both ACC and LCC. Cluster analysis identified four distinct groups of drivers based on their knowledge levels. Key predictors of a driver’s mental model included years of licensure, weekly driving distance, ADAS familiarity, driving style (planning), and personality traits (agreeableness). Regarding trust, the study found that a driver’s mental model, vehicle brand, age, ADAS experience, driving style (focus), and emotional stability were significant predictors. Notably, the relationship between mental model and trust was complex; while a better mental model was generally associated with lower trust (suggesting calibrated skepticism), this relationship was moderated by experience and other dispositional factors. The findings highlight that even experienced users struggle to build well-calibrated mental models for advanced automation, posing safety risks due to potential over-trust or misuse. The study underscores the importance of cultural context, as Chinese drivers’ perceptions differ from those in North America or Europe. These insights are significant for the automotive industry and policymakers, suggesting that driver education programs must move beyond basic ACC/LCC training to address the specific capabilities and limitations of emerging ADAS features. By identifying the specific demographic and psychological factors that influence trust and knowledge, manufacturers can design more effective in-vehicle interfaces and training materials to ensure safe human-automation interaction.

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Topics

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• trust calibration
• situational awareness
• automation
• automation surprise
• trust in automation foundations
• acceptance adoption

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• Empirical Findings: self report data
• Theoretical Contribution: computational model, theory or model