B11 Driving Behavior Analysis for Accident Prone Area using Realtime Simulator
DOI: 10.1299/jsmemovic.2005.9.225
archive: archived pipeline: cataloged verified
Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)
Summary
This study addresses the persistent issue of traffic accidents on the Meihan Highway in Japan, specifically focusing on the "Omega curve," a section known for its high accident frequency despite a national trend of decreasing traffic fatalities. The research is motivated by the need to understand the mechanisms of unsafe driving behavior in this complex road environment, where chronic overspeeding and intricate road geometry are primary accident factors. Since conducting experiments in the actual environment is dangerous, the authors utilized a real-time driving simulator to safely reproduce the Omega curve and analyze the driving behaviors that lead to accidents. The experimental design involved constructing a real-time driving simulator using an A&D DSP AD5410 and a simulation PC running RT-Linux. The vehicle dynamics were calculated using CarSim software, while the visual environment was generated using actual road data, including X-Y coordinates and longitudinal and transverse gradients, to accurately replicate the 3D road shape of the Omega curve. The simulator interface included force-feedback steering, accelerator, and brake pedals connected via input/output boards to capture driver inputs. The study specifically targeted the sharp curve sections within the Omega curve, which account for approximately 50% of accidents on the Meihan Highway. The researchers analyzed the impact of entry speed into these curves on driving behavior, noting that the downhill line experiences significantly more accidents than the uphill line due to higher speeds and vehicle instability. The findings highlight that the Omega curve’s complex geometry, characterized by high curvature and rapid changes in curvature, leads to steering errors and subsequent collisions with side walls or skidding. The study identified four main accident factors: overspeeding, steep gradients, sharp curves, and the mismatch between actual vehicle speed and road structure. Specifically, the downhill section features steep gradients (1–6%) that cause vehicles to exceed 90 km/h, far above the 60 km/h limit. Drivers often decelerate abruptly (10–15 km/h) upon entering sharp curves, which increases the risk of rear-end collisions from following vehicles. The simulator experiments allowed for the observation of these unsafe behaviors, confirming that the combination of high entry speeds and the road’s geometric complexity creates unstable vehicle dynamics, particularly on the downhill line, where rear-wheel vertical load decreases, leading to oversteer tendencies. The significance of this research lies in its contribution to accident prevention strategies for complex road sections. By identifying the specific driving behaviors and mechanical factors that contribute to accidents in the Omega curve, the study provides a basis for developing targeted countermeasures. The use of a real-time simulator with accurate road data demonstrates a viable method for analyzing accident mechanisms without the risks associated with real-world testing. This approach can inform the design of advanced highway systems and infrastructure improvements to mitigate the high accident rates in such prone areas.
Key finding
High entry speeds into sharp curves on steep downhill gradients cause unstable vehicle dynamics and steering errors, leading to accidents in the Omega curve area.
Methodology
simulator
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 | — | — | 1 | 2026-06-04 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| 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 | 2026-05-28 |
| 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.
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, dataset resource
- Theoretical Contribution: computational model