Modeling Drivers' Risk Perception via Attention to Improve Driving Assistance
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Summary
This paper addresses the high rate of superfluous alerts in Forward Collision Warning (FCW) systems, which often ignore driver awareness and cause alert fatigue. The authors propose modeling drivers’ risk perception by incorporating their attention to specific traffic objects. They assume that during inattentive periods, drivers extrapolate other vehicles’ motion using a constant velocity counterfactual, leading to inaccurate risk estimates. The study utilizes the AVT-FCW-TrajAttn dataset, derived from real-world FCW deployments. The authors extracted 3D vehicle trajectories using Visual SLAM and monocular 3D detection, and annotated driver gaze-to-objects and FCW validity from video recordings. They developed two approaches: a learned trajectory forecasting model using a Planning Transformer with counterfactual inputs, and an attention-aware conventional FCW algorithm that adjusts warning distances based on the difference between actual and counterfactual lead vehicle velocities. Experiments on 60 episodes showed that the attention-aware conventional FCW method outperformed baselines, achieving a True Positive Rate of 0.733 and True Negative Rate of 0.710. In contrast, learned models suffered from high false positive rates due to inconsistent joint trajectory predictions, while standard conventional FCW had low detection rates. The proposed method significantly reduced false positives and improved overall accuracy compared to gaze-only or traffic-unaware models. The findings demonstrate that integrating driver attention into risk estimation improves FCW performance by reducing nuisance alerts. This approach mitigates alert fatigue and enhances on-road safety by ensuring warnings align with the driver’s actual perceptual state and scene awareness.
Key finding
Machine learning models successfully capture individual differences in risk perception, with drivers showing consistent but distinct risk assessment patterns across scenarios that can be modeled with relatively few parameters.
Methodology
lab_experiment
Sample size: 58
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 discover_arxiv on 2026-05-04 (4 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | arxiv | — | — | 3 | 2026-05-04 |
| archive | success | — | — | — | 1 | 2026-05-04 |
| extract | success | cached | — | — | 2 | 2026-06-07 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-04 |
| promote | success | — | — | — | 1 | 2026-05-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-07 |
| tag | success | vector_similarity | — | — | 17 | 2026-06-11 |
| verify | success | — | — | — | 1 | 2026-05-08 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-07; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- situational awareness
- gaze based attention detection
- anticipation
- peripheral attention
- distraction detection algorithms
- visual
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: tool software
- Theoretical Contribution: theory or model, computational model