Enhancing Passenger Trust Toward Cooperative Autonomous Vehicles Using Simulated Augmented Reality Displays
Bibliographic record
Abstract
Adoption of Fully Autonomous Vehicles (FAVs) depends on trust, which is defined as confidence in a vehicle's dependability, safety, and predictability.In cooperative driving scenarios, trust must exceed ego vehicles to include other autonomous vehicles and their coordination.This is challenged by unexpected multi-agent interactions, diminishing human control, and limited system transparency.We hypothesize that enhancing transparency by providing information about ego vehicle, other cooperative vehicles, and road conditions can foster trust.This is achieved by visualizing vehicleto-everything (V2X) information via augmented reality (AR) interfaces.To test this in a safe environment, we conducted a withinsubjects experiment in a Virtual Reality (VR) driving simulator with AR overlays.Participants experienced three interface concepts: (A) no transparency, (B) system-level transparency (ego vehicle intentions only), and (C) environment-level transparency (cooperation intentions, planned paths, and infrastructure).Results show that environment-level transparency, despite the higher cognitive workload, enhanced trust in both ego and cooperating FAVs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".