Subtle motion cues by automated vehicles can nudge human drivers’ decisions: Empirical evidence and computational cognitive model
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Summary
This study investigates how automated vehicles (AVs) can proactively influence human driver decisions through implicit communication, specifically in scenarios where the AV holds the right of way. While existing research often focuses on AVs yielding to humans, this work addresses the gap in understanding how AVs might "nudge" human behavior to reduce uncertainty and improve interaction safety. The researchers examined whether subtle longitudinal maneuvers—brief decelerations or accelerations—could alter a human driver’s decision to execute an unprotected left turn across the path of an oncoming AV. The study employed a driving simulator experiment with 19 participants who completed 3,800 left-turn trials. The experimental design manipulated two variables: the AV’s initial time-to-arrival (TTA) at the intersection (4.5s or 5.5s) and its acceleration profile. Five AV behavior conditions were tested: constant speed (baseline), deceleration nudge, acceleration nudge, long deceleration, and long acceleration. Dependent variables included the decision outcome (Go or Stay), response time, and subjective negative ratings of the AV’s behavior. The researchers analyzed the data using mixed-effects regression models and fitted the results to variants of a drift-diffusion model, a computational framework for evidence accumulation in decision-making. The results demonstrated that human drivers were sensitive to deceleration nudges but not acceleration nudges. Specifically, the probability of a driver choosing to "Go" (turn before the AV) significantly increased when the AV performed a brief deceleration nudge compared to constant speed. Conversely, acceleration nudges had no statistically significant effect on decision outcomes. Long deceleration maneuvers also increased the likelihood of a Go decision but induced significantly longer response times and elicited high rates of negative subjective ratings, indicating that prolonged deviations were perceived as confusing or unintuitive. The drift-diffusion modeling revealed that the most parsimonious explanation for this behavior involved noisy integration of dynamic time-to-arrival and distance information against a fixed decision boundary, with an initial bias toward the Go decision. The significance of this work lies in providing empirical evidence that subtle, transient AV maneuvers can effectively nudge human driver behavior without causing confusion or negative reactions. Unlike prolonged maneuvers, brief deceleration nudges successfully influenced decision-making while maintaining user acceptance. Furthermore, the validated computational cognitive model offers a tool for predicting human responses to arbitrary AV maneuvers. This framework can inform the design of interaction-aware AV controllers, enabling them to proactively manage traffic interactions by leveraging implicit communication strategies that align with human cognitive processes.
Key finding
Automated vehicles performing brief deceleration nudges significantly increase the likelihood that human drivers will accept a gap and proceed with an unprotected left turn, while acceleration nudges do not significantly affect this decision.
Methodology
simulator
Sample size: 19
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-05 |
| archive | success | canonical_url | — | — | 7 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-05 |
| chunk | success | chunk | — | — | 1 | 2026-06-05 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-05 |
| promote | success | — | — | — | 1 | 2026-06-05 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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Information type
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- Empirical Findings: behavioral performance data
- Theoretical Contribution: computational model, theory or model