3112 Driving behavior due to the difference of control algorithm of collision-prevention braking
DOI: 10.1299/jsmetld.2011.20.369
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Summary
This study investigates how different control algorithms for collision-prevention support braking systems influence driver behavior, particularly regarding risk-taking actions when the system malfunctions. The research aims to quantify the accident mitigation effects of such systems by analyzing driver braking timing and deceleration levels under various system conditions. The motivation stems from the need to understand potential over-dependence on automated systems and to propose a methodology for estimating collision mitigation ratios that accounts for driver-system integrated error. The researchers conducted experiments using a driving simulator with 15 young male subjects who regularly drive. Three distinct system configurations were tested, varying in alarm timing, braking initiation time, deceleration levels, and stopping distances. The experimental design included four operational states: normal operation, malfunction without notification, malfunction with notification (via an LED indicator), and no system usage. The scenario involved a rear-end collision risk where the preceding vehicle decelerated at 4 m/s². Driver braking behavior was recorded during these trials. Subsequently, a driver model simulating braking operations was constructed based on the experimental data. Time-series Monte Carlo simulations were performed using this model to estimate collision frequency and collision velocity for each system specification. The results indicated significant differences in driver behavior based on system specifications and operational status. During normal operation, drivers using System 1 (earlier alarm and braking) exhibited longer reaction times to alarms compared to System 3, with 11% of trials showing no braking action by the driver, suggesting reliance on the system. When the system malfunctioned without notification, drivers showed delayed braking initiation and increased jerk (rate of change of acceleration) compared to driving without any system. However, when drivers were notified of the malfunction via an LED indicator, their braking reaction times and jerk values returned to levels comparable to driving without the system. This indicates that notification effectively mitigates the negative behavioral changes caused by system failure. The study concludes that informing drivers of system malfunctions is crucial for preventing risk-taking behaviors associated with over-dependence. The proposed methodology, combining simulator experiments with Monte Carlo simulations, allows for the quantitative estimation of collision mitigation ratios. This approach provides a framework for evaluating the effectiveness of collision-prevention systems by accounting for human factors and system reliability, which is essential for the practical implementation and promotion of such safety technologies.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-19 |
| archive | success | canonical_url | — | — | 1 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-20 |
| chunk | success | chunk | — | — | 1 | 2026-06-20 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-20 |
| enrich | success | openalex | — | — | 1 | 2026-06-20 |
| promote | success | — | — | — | 1 | 2026-06-19 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-20 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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- Empirical Findings: behavioral performance data
- Theoretical Contribution: computational model