A framework for context-aware driver status assessment systems
DOI: 10.13140/rg.2.1.1279.3762
archive: archived pipeline: cataloged verified
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Summary
This thesis addresses the critical safety issue of driver-related accidents, which account for 80–90% of fatal and injury crashes, primarily due to fatigue and distraction. Motivated by the need for intelligent transportation systems that can assess driver alertness in real-time, the research proposes a modular, context-aware framework for driver status assessment. The system aims to utilize multi-sensor data fusion—combining audio, video, and contextual information—to identify drivers and detect states of inattention, thereby enabling proactive safety interventions. To implement this framework, the author constructed a driving simulator equipped with multiple sensors, including a microphone array, a near-infrared camera, a Kinect sensor, and a heart rate monitor. The experimental design focused on three core modules: driver identification, fatigue detection, and distraction recognition. For identification, the system employed audio-visual data fusion, combining voice recognition (using Mel-Frequency Cepstral Coefficients) and face recognition (using Gradientfaces) to verify user identity and load personalized background context. Fatigue detection relied on an infrared camera to track eye movements, calculating the Percentage of Eye Closure (PERCLOS) as the primary indicator of drowsiness. Distraction assessment utilized the Kinect sensor to analyze body posture, head pose, and facial expressions, employing feature extraction techniques such as arm position recognition and gaze estimation. Experimental results demonstrated the efficiency of the proposed system in both driver identification and inattention detection. The audio-visual fusion module successfully identified drivers, leveraging complementary data to overcome challenges such as in-car noise and illumination variations. The fatigue detection module effectively tracked eye closure using particle filters, providing reliable PERCLOS metrics. For distraction, the system achieved accurate recognition of various distraction types by fusing features from arm position, eye behavior, and facial expressions using AdaBoost classifiers and Hidden Markov Models. The modular architecture allowed for distinct analysis of these states, confirming that multi-modal fusion improves robustness compared to single-sensor approaches. The significance of this work lies in its contribution to the development of context-aware Advanced Driver Assistance Systems (ADAS). By integrating driver identification with status monitoring, the framework enables personalized and more accurate assessment of driver behavior. The thesis concludes that while fatigue and distraction are only a subset of possible driver states, the modular design allows for future expansion to include additional sensors and context integration, such as GPS data or social information. This approach supports the broader goal of creating intelligent vehicles capable of predicting and mitigating dangerous driving behaviors, ultimately enhancing road safety.
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 | openalex | — | — | 9 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| 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 | — | — | — | 1 | 2026-05-27 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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- Methodological Resource: tool software, validation psychometrics
- Theoretical Contribution: conceptual framework