DriveSAM: Cognitive Perspective on Driving Maneuvers Based on Drivers’ Attention Using Eye Gaze Data
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Summary
This study addresses the critical need to understand driver behavior and cognitive decision-making by analyzing how drivers allocate visual attention during various driving maneuvers. The authors aim to answer fundamental questions regarding what captures drivers' primary visual focus and why drivers occasionally deviate into obstacles or other vehicles. To bridge gaps in previous research, the study employs a zero-shot learning technique to uncover complex patterns between driver attention and cognitive processes, leveraging the synergistic fusion of image features from driving scenes and eye gaze data. The methodology utilizes the DR(eye)VE dataset, which comprises 555,000 frames across 74 sequences of five-minute durations. The data involves eight drivers (seven male, one female, aged 20–40) navigating diverse environments, including downtown areas, countryside settings, and highways, under varying weather and lighting conditions. The core analytical tool is the Segment Anything Model (SAM), a deep learning architecture designed for zero-shot learning. SAM consists of an image encoder using a pre-trained Vision Transformer, a flexible prompt encoder that processes sparse prompts (points, boxes, text) and dense prompts (masks), and a mask decoder that maps these embeddings to segment attention regions. This approach allows the model to detect and segment objects without predetermined labels, generating attention heatmaps and density maps based on driver gaze information. The results indicate a distinct variance in attention allocation depending on the driving context. Drivers predominantly focused their attention on the road ahead during highway driving, a behavior attributed to higher speeds, the necessity of maintaining safe following distances, and fewer distractions. In contrast, city driving environments, characterized by pedestrians, intersections, and frequent stops, presented more complex attention patterns. The study also found that instances where drivers veered into other lanes or toward obstacles often coincided with their gaze being diverted away from the primary roadway, linking gaze diversion directly to hazardous maneuvers. The significance of this research lies in its demonstration that zero-shot learning can effectively segment driver attention without extensive labeled data, offering a scalable method for behavioral research. The findings highlight the importance of context-specific interventions for road safety. For instance, adaptive driver assistance systems could be tailored to provide more frequent alerts in city environments where distraction risks are higher. By elucidating the relationship between visual attention and decision-making, this work contributes to the development of optimized driver monitoring systems and advanced human-vehicle interaction strategies.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-25 |
| archive | success | canonical_url | — | — | 1 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-26 |
| chunk | success | chunk | — | — | 1 | 2026-06-26 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-26 |
| enrich | success | openalex | — | — | 1 | 2026-06-26 |
| promote | success | — | — | — | 1 | 2026-06-25 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-26 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- gaze based attention detection
- attention allocation
- eye movements scanning
- peripheral attention
- situational awareness
- distraction detection algorithms
Information type
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- Empirical Findings: behavioral performance data
- Methodological Resource: tool software
- Theoretical Contribution: theory or model