A Perceptually Inspired Driver Model for Speed Control in Curves
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Summary
This paper addresses the need for more accurate models of driver speed control in curves, which are essential for road safety analysis and the development of Advanced Driver Assistance Systems (ADAS). Existing models primarily correlate road geometry with observed speeds, failing to capture individual driver preferences or the specific longitudinal control inputs (accelerator and brake pedal actuation) used to negotiate turns. The authors propose a perceptually inspired model based on the Time to Extended Tangent Point (TETP), a visual metric representing the time required to reach a specific point on the road horizon. This approach leverages the finding that humans accurately judge time margins to visual targets, despite poor performance in judging static curvature. The study develops a five-phase speed control model (acceleration, deceleration, braking, brake release, and re-acceleration) governed by three TETP thresholds and three actuation gains. To validate this model, the researchers conducted a simulator experiment with 15 participants driving on roads with varying widths, radii, and deflection angles. The experimental design aimed to demonstrate that TETP triggers explain speed adaptation phases more accurately than alternative metrics like Time to Line Crossing (TLC). Model parameters were individualized for each driver using a binary classification method (Matthews Correlation Coefficient) to fit deceleration and braking thresholds, followed by a constrained least squares fit for pedal gains. Results indicate that TETP is a superior predictor of driver behavior compared to TLC. The model successfully captured the distinct phases of speed adaptation, with drivers releasing the accelerator at a higher TETP threshold (mean 4.67 s) and applying brakes at a lower threshold (mean 3.26 s). Validation on isolated turns showed that the individualized model accurately tracked driver speed choice, achieving an average Variance Accounted For (VAF) above 60% for speed. While pedal deflection predictions were fair, the model effectively reproduced the timing of braking events and the transition between coasting and acceleration. The study concludes that a TETP-based model, when individualized, can accurately represent how drivers perceive and react to road geometry, offering a robust framework for simulating naturalistic driving behavior in ADAS applications.
Key finding
A driver model based on Time to Extended Tangent Point thresholds accurately captures individual speed adaptation and pedal control strategies during curve negotiation in a driving simulator.
Methodology
simulator
Sample size: 15
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 | canonical_url | — | — | 7 | 2026-06-06 |
| 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 | 2026-05-27 |
| 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.
- speed choice
- perception action locomotion
- steering pattern
- perceptual countermeasures
- mental model of traffic
- 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: tool software
- Theoretical Contribution: computational model, theory or model