Modeling driver control behavior in both routine and near-accident driving
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Summary
This paper addresses a discrepancy in existing driver modeling literature, which typically treats routine driving and near-accident scenarios as distinct phenomena requiring different control models. Routine driving is often modeled as continuous, well-tuned closed-loop control, whereas near-accident behavior is characterized by long-latency, ill-tuned open-loop maneuvers. The author proposes a unified framework based on contemporary neuroscience, specifically accumulator models of sensorimotor behavior, to explain both contexts through a single set of underlying assumptions. The central hypothesis is that driving control consists of discrete, open-loop adjustments triggered by the accumulation of sensory evidence, rather than continuous feedback loops. The methodology involves applying accumulator models to explain the timing and magnitude of driver control adjustments. The framework posits that an adjustment occurs when integrated sensory evidence for a need to act reaches a threshold. This process is influenced by noise, gating mechanisms, and expectancy. The paper validates this approach by fitting the accumulator model to existing experimental data, specifically detection thresholds for optical expansion from Lamble et al. (1999) and brake onset timing from Kiefer et al. (2003). Additionally, the author illustrates the framework using simulations of braking behavior, incorporating forward-model predictions to account for how drivers anticipate the sensory consequences of their actions. The results demonstrate that the accumulator model successfully accounts for variance in looming detection thresholds and brake onset timing. Specifically, the model explains why drivers react at lower optical expansion rates when initial headways are longer, as the integration of small stimuli over time is equivalent to integrating large stimuli over short periods. The framework also resolves the apparent contradiction between routine and emergency control: routine driving appears continuous because frequent, small adjustments are superimposed, while near-accident scenarios reveal the underlying open-loop nature of control due to the urgency and magnitude of required adjustments. Simulations show that the model can reproduce underreactions followed by corrective increases in pedal position during strong deceleration events, driven by the interplay between sensory input and forward-model predictions. The significance of this work lies in providing a unified theoretical basis for driver behavior modeling. By linking routine and near-accident control to the same neurobiological mechanisms of evidence accumulation and motor primitives, the framework allows for more accurate simulation models. It suggests that future models should incorporate situation-dependent distributions for response times and maneuver amplitudes, rather than treating them as fixed parameters. This approach improves the ability to simulate driver responses in both safe and critical traffic situations, offering better tools for road safety research and the development of advanced driver assistance systems.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | OpenAlex-citations | — | — | 1 | 2026-06-25 |
| archive | success | unpaywall | — | — | 2 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-25 |
| chunk | success | chunk | — | — | 1 | 2026-06-25 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-25 |
| promote | success | — | — | — | 1 | 2026-06-25 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-25 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- braking response
- mental model of traffic
- steering pattern
- anticipation
- situational awareness
- perception action locomotion
- human error taxonomy
Information type
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- Empirical Findings: behavioral performance data
- Theoretical Contribution: computational model, theory or model