Eye-Tracking Study Reveals Trust, Not Gaze, Drives Feeling of Safety in Self-Driving Cars

Autonomous vehicles are becoming statistically safer than the humans they replace, yet a sweeping new cross-cultural study suggests that the feeling of safety is an entirely different matter. In one of the largest eye-tracking investigations of its kind, researchers collected data from 1,272 participants across 22 cities on five continents, recording roughly 4,000 gaze-based indicators as people viewed traffic scenarios involving both autonomous and conventional vehicles. Their findings, published in the journal AI & Society, deliver a provocative message: what people look at has remarkably little to do with whether they feel safe.

The study arrives at a moment of tension in the deployment of self-driving technology. On the technical side, the numbers are impressive. Drawing on prior real-world evidence, the authors note that across 56.7 million rider-only miles, autonomous systems showed crash-rate reductions of roughly 79 percent for injury-reported crashes, 81 percent for airbag-deployment crashes, and 85 percent for suspected serious-injury crashes compared with human benchmarks. Bodily-injury insurance claims in fully driverless operations fell by as much as 92 to 93 percent, and police-reported injury crashes dropped by about 55 percent in some analyses. Since human error contributes to roughly 94 to 95 percent of road collisions, these gains represent a genuine transformation in road safety.

Yet the study argues that functional safety and perceived safety are fundamentally distinct constructs, and that conflating them is a mistake with real consequences for public acceptance. The researchers, led by Anu Masso of Tallinn University of Technology together with colleagues in Hong Kong and Denmark, designed a semi-experimental online study in which participants viewed paired traffic scenarios presented under both autonomous and non-autonomous conditions. The scenarios deliberately featured a wide range of social diversity cues, including gender, age, race operationalized through skin-tone variation, income, disability, and micromobility, grounded in theories of super-diversity and intersectionality. Areas of interest were matched in size and position across paired images to rule out low-level visual saliency as a confound.

Eye tracking was conducted through the RealEye platform, which uses webcam-based AI gaze prediction with an accuracy of roughly 110 pixels. Each trial began with a 40-point calibration and a 9-point validation check, and participants whose calibration errors exceeded 2.5 degrees of visual angle or who failed attention checks were excluded. The primary measure was fixation total time, the cumulative dwell time spent on each social diversity area of interest, chosen for its robustness in webcam-based settings and its interpretability as sustained attentional engagement. Revisit counts served as secondary validation. The team then used Bayesian multilevel regression models, estimated with four Markov Chain Monte Carlo chains in the brms package, to link attention patterns with self-reported safety and security evaluations while accounting for individual and city-level variation.

The first major finding concerns the structure of visual attention itself. Gaze patterns toward social groups proved to be highly organized and remarkably stable across both autonomous and non-autonomous scenarios. Categories such as age, race, and income consistently attracted higher levels of attention in both contexts, while disability-related and COVID-related cues remained comparatively backgrounded. Automation modestly increased overall attention to social cues without fundamentally reshaping these hierarchies. The estimated association between attention indices in the two contexts was moderate, with a beta of approximately 0.66 and a 95 percent confidence interval spanning 0.44 to 0.88, indicating that attentional structure is partially stable but also partially reconfigured by mobility context. In other words, autonomous mobility amplifies existing perceptual salience hierarchies rather than creating new ones.

The surprise came when the researchers connected those gaze patterns to feelings of safety. At the individual level, correlations between visual attention indices and perceived safety or security were weak, with absolute correlation values below 0.2, and scatter plots revealed no clear linear associations. In the final Bayesian models, the key predictor was the difference in attention between autonomous and non-autonomous contexts, and its posterior estimates were centered near zero with credible intervals overlapping zero for both outcomes. Once trust, social positioning, and regional context were accounted for, shifts in visual attention simply did not translate into changes in experienced safety or normative security. Being perceptually alert to social difference, it turns out, is not the same as feeling protected by it.

What did predict perceived safety? Trust emerged as the dominant force. Both general trust in automation, measured across SAE automation Levels 0 through 5, and situational trust in autonomous transport applications showed consistently positive posterior estimates for experienced safety. Notably, trust remained relatively stable across automation levels 0 to 4, with mean scores between 3.40 and 3.49, but dropped to 3.10 for Level 5 full automation, signaling persistent hesitation toward fully driverless systems even among people otherwise comfortable with semi-automated technology. Trust also varied by who was being transported: respondents expressed the highest trust in autonomous vehicles carrying people from rural to urban areas and the lowest in vehicles carrying elderly people to parks, suggesting that acceptance depends on the perceived vulnerability and social status of passengers.

Security values followed a different explanatory logic altogether. Unlike perceived safety, security as a normative principle was less strongly associated with trust and more strongly shaped by social position and cultural context. Minority status, measured through self-identification as belonging to a national minority group, was the most robust individual-level predictor, with minority respondents systematically assigning lower importance to security values and reporting lower perceived safety even after controlling for trust and attention. Regional effects were substantial in both models, with patterns more pronounced for security values than for perceived safety, reinforcing the interpretation that security evaluations are deeply anchored in cultural and institutional environments. Higher perceived safety was generally reported by respondents with higher education and by those living in European and East and Southeast Asian regions, while younger respondents aged 18 to 29 and those in the Americas or Sub-Saharan Africa reported lower levels.

The broader survey results add texture to this picture. Nearly 30 percent of respondents felt that transport safety in their city had remained unchanged over the previous five years, while roughly 55 percent perceived an increase and about 16 percent a decrease, indicating that even perceptions of objective safety trends are filtered through local experience. Meanwhile, security as a public value was overwhelmingly salient: 54.7 percent rated it as very important in the development of AI-based urban technologies, and only 13 percent considered it insignificant. These patterns suggest that while people care intensely about security in the abstract, their concrete feelings of safety in automated mobility are constructed through trust, social identity, and institutional context rather than through what their eyes happen to land on.

The implications for the autonomous vehicle industry and for AI ethics are significant. A widespread assumption holds that making diverse social groups visible and recognizable within mobility environments is sufficient to foster inclusion, trust, and safety. This study challenges that assumption directly. Visual representation may be necessary for functional safety, ensuring that systems reliably detect vulnerable road users, but it does not guarantee psychological reassurance or normative legitimacy. The authors call for complementing safety-by-design with socio-psychological safety-by-design, in which trust calibration, cultural legibility, and social recognition extend beyond what systems detect or display. The study also acknowledges limitations, including the lower spatial precision of webcam-based eye tracking compared with laboratory systems, the possibility that visual stimuli unintentionally encoded stereotypes, and small sample sizes in some cities, which the authors address through partial pooling in their Bayesian models. Future research, they suggest, should combine laboratory-grade eye tracking, in situ mobility experiences, and qualitative approaches to uncover the mediating mechanisms, such as institutional trust and prior experience with automation, that connect perception to judgment. For now, the message is clear: legitimate autonomous mobility will not be won by crash statistics or visible diversity alone, but by the social and institutional conditions under which people come to feel genuinely safe.

Subject of Research: Cross-cultural eye-tracking research on perceived safety and trust in autonomous vehicle environments

Article Title: What shapes perceived safety in autonomous mobility? Cross-cultural evidence from large-scale online eye tracking

Article References: Masso, A., Pietarinen, A.-V., Ibrahimi, M., & Ben Yahia, S. (2026). What shapes perceived safety in autonomous mobility? Cross-cultural evidence from large-scale online eye tracking. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03278-1

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03278-1

Keywords: autonomous vehicles, eye tracking, perceived safety, trust in automation, AI ethics, cross-cultural study, social diversity, Bayesian multilevel models, data justice, minority status, human-machine interaction, urban mobility