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 when they have priority, such as during unprotected left turns. The authors aim to determine if subtle longitudinal maneuvers by an AV can alter a human driver’s decision to proceed or wait, and to develop a computational cognitive model that explains these interactions. The researchers conducted a driving simulator experiment with 19 participants who performed unprotected left turns across the path of an oncoming AV. The study utilized a 2 × 5 within-subject design, manipulating the AV’s initial time-to-arrival (4.5 s or 5.5 s) and its acceleration profile. The five acceleration conditions included a constant speed baseline, a brief deceleration nudge, a brief acceleration nudge, and longer continuous deceleration or acceleration maneuvers. The AVs executed these nudges by briefly changing speed and then returning to their original velocity. The primary dependent variables were the driver’s decision outcome (Go or Stay), response time, and subjective negative ratings of the AV’s behavior. Statistical analyses employed mixed-effects regression models, and the data was fitted to variants of a drift-diffusion model to simulate 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" (accept the gap) significantly increased when the AV performed a deceleration nudge compared to the constant speed baseline. Conversely, acceleration nudges had no statistically significant effect on decision outcomes. Long deceleration maneuvers also increased the likelihood of a Go decision, while long acceleration decreased it. Regarding response times, Go decisions were generally faster than Stay decisions. Notably, long deceleration induced significantly longer response times for Go decisions, particularly when the initial time-to-arrival was larger. Subjectively, participants rated long deceleration maneuvers as significantly more negative and confusing, whereas brief deceleration nudges elicited only slightly more negative reactions than constant speed. The study concludes that subtle longitudinal deceleration by an AV can effectively nudge human drivers to proceed, whereas acceleration does not have a comparable effect. The authors developed a parsimonious drift-diffusion model that accurately captured the observed behavior, hypothesizing that drivers integrate noisy dynamic information about time-to-arrival and distance against a fixed decision boundary, with an initial bias toward proceeding. This model provides a computational framework for predicting human responses to arbitrary AV maneuvers, offering valuable insights for designing interaction-aware controllers that can proactively manage traffic interactions safely and intuitively.
Key finding
Automated vehicles performing brief deceleration maneuvers significantly increase the likelihood that human drivers will proceed with an unprotected left turn, while acceleration maneuvers do not significantly alter decision outcomes.
Methodology
simulator
Sample size: 19
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via author_sweep_intake on 2026-05-27.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 2 | 2026-05-27 |
| archive | success | canonical_url | — | — | 5 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-07 |
| chunk | success | chunk | — | — | 1 | 2026-06-07 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-07 |
| enrich | skipped | — | — | — | 4 | 2026-07-02 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| 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.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- situational awareness
- automation surprise
- mental model of traffic
- gap acceptance
- anticipation
- driver vru interaction
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Empirical Findings: behavioral performance data
- Theoretical Contribution: computational model, theory or model