A risk field-based metric correlates with driver’s perceived risk in manual and automated driving: A test-track study
DOI: 10.1016/j.trc.2021.103428
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Summary
This study investigates whether a computational metric, the Driver’s Risk Field (DRF) risk estimate ($\hat{r}$), correlates with drivers’ perceived risk during both manual and automated driving. The research addresses the need for objective measures to evaluate automated vehicle (AV) behavior and predict driver takeovers, as subjective risk perception is critical for user trust and safety. Previous models often used fragmented metrics for specific scenarios; this work tests a unified DRF model, which combines the consequence of potential collisions with the probability of their occurrence, against real-world driving data. The experiment was conducted on a test track using a Nissan Leaf equipped with an automated driving system. Eight certified male drivers participated, completing five laps of manual driving and twelve laps of automated driving. The track featured three distinct sections subdivided into twelve sectors: curves with varying curvatures, a parked car outside the lane boundary, and 90-degree intersections. During automated laps, drivers were instructed to think aloud, providing verbal comments on their perceived risk, and were permitted to take over control if they felt unsafe. The DRF risk estimate was calculated offline using high-definition maps and GPS data, modeling risk based on environmental costs (e.g., hitting a curb vs. staying on road) and a dynamic field representing the vehicle’s predicted path and uncertainty. Results demonstrated that the DRF risk estimate effectively predicted manual driving behavior, showing moderate-to-strong correlations with steering angle ($\rho_{steering} = 0.69$) and speed reduction ($\rho_{speed} = 0.64$). In automated driving, the metric strongly correlated with drivers’ perceived risk. Specifically, the maximum risk estimate per sector correlated highly with risky verbal comments during curve driving ($r^2 = 0.98$) and when negotiating the parked car ($r^2 = 0.59$). Drivers frequently identified sectors with high $\hat{r}$ values as risky, particularly when the AV drove close to curbs. Conversely, sectors with low $\hat{r}$ values generally elicited non-risky comments or no comments. The study found that lateral positioning relative to obstacles significantly influenced perceived risk more than curvature alone in certain scenarios. The findings confirm that the DRF-based risk estimate is a valid predictor of both manual driving actions and subjective risk perception in automated driving contexts. This unified metric offers a valuable tool for designing and evaluating AV behaviors, potentially aiding in the development of systems that maintain user trust by keeping perceived risk within acceptable thresholds. The authors conclude that future research should incorporate tactical and strategic driving components to further refine the model’s applicability.
Key finding
The Driver’s Risk Field metric significantly predicted manual driving behavior and correlated with drivers' perceived risk during automated driving, particularly in curve and parked car scenarios.
Methodology
on_road
Sample size: 8
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 openalex_abstract on 2026-05-08.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | — | — | — | 1 | 2026-05-07 |
| archive | success | canonical_url | — | — | 7 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | openalex | — | — | 2 | 2026-05-08 |
| promote | success | — | — | — | 1 | 2026-05-07 |
| 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.
- risk taking
- situational awareness
- automation surprise
- decision making risk perception
- exposure measurement
- acceptance adoption
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).
- Methodological Resource: validation psychometrics
- Theoretical Contribution: computational model, theory or model