Identification of Human Driver Critical Behaviors and Related Reliability Evaluation in Real Time
DOI: 10.3850/978-981-18-5183-4_s06-09-254
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Summary
This paper addresses the critical need for real-time evaluation of human driver reliability, particularly as automation shifts driver roles from active control to passive monitoring. With human factors cited as the cause of 94% of traffic accidents, the authors aim to identify critical driver behaviors and evaluate performance dynamically. Previous Human Reliability Analysis (HRA) methods were largely static, failing to account for the continuous evolution of driving contexts. To bridge this gap, the study applies a modified fuzzy-based Cognitive Reliability and Error Analysis Method (CREAM) to generate a Human Performance Reliability Score (HPRS) that updates on a second-by-second basis. The methodology utilizes driving data collected from a SCANeRT M studio simulator, involving a driver with eight years of experience. The approach employs the FN-DBSCAN (fuzzy neighborhood density-based spatial clustering of application with noise) algorithm, optimized by a genetic algorithm, to automatically generate membership functions for Common Performance Conditions (CPCs). These CPCs include ego-vehicle speed, time to collision (TTC), longitudinal and lateral acceleration, traffic density, and visibility. By clustering this data, the system assigns reliability effects (improved, not significant, or reduced) to specific driving states, allowing for the continuous calculation of CPC scores and the aggregate HPRS. The results demonstrate the system's ability to detect critical situations in real time. In a specific analysis of a driving maneuver between 409.1 and 411.6 seconds, the HPRS fluctuated significantly, revealing a critical error. The driver initially misjudged the decreasing TTC, failing to recognize the proximity to the front vehicle despite braking. This misjudgment led to a sudden, hard braking event (longitudinal acceleration of -10.2 m/s²) and significant lateral acceleration fluctuations, indicating driver stress. Using the Stanton and Salmon taxonomy, this behavior was classified as a "misjudgment" error related to situation assessment. Generally, the HPRS values remained above the strategic control level, indicating reliable performance, except during these critical peaks and valleys. The significance of this work lies in its provision of a dynamic, measurement-based tool for supervising human drivers in situated contexts. By moving beyond static HRA models, the approach allows for the real-time identification of critical behaviors and potential errors. This capability is vital for the development of advanced driver assistance systems (ADAS) and automated vehicles, where monitoring human vigilance and readiness to take over is essential. The authors suggest future work should expand the dataset to include multiple drivers and compare control mode levels with other frameworks like the SRK model to further refine critical situation estimation.
Key finding
The modified fuzzy-based CREAM approach successfully identified a driver's misjudgment of time-to-collision as a critical behavior causing a sharp decline in real-time human performance reliability.
Methodology
simulator
Sample size: 1
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 2026-05-28.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 2 | 2026-05-28 |
| archive | success | canonical_url | — | — | 7 | 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 | failed | — | — | — | 5 | 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.
- human error taxonomy
- pre crash contributing factors
- automation surprise
- crash reconstruction hf
- naturalistic crash near crash
- situational awareness
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