Drivers with limited perception: model and application to traffic simulation
DOI: 10.4074/s0761898014001046
archive: archived pipeline: cataloged verified
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Summary
This paper addresses the limitations of existing microscopic traffic simulation models, which often assume drivers possess unlimited perceptual capabilities and perfect information processing. The authors argue that this "omniscient" assumption fails to capture realistic driver behavior, particularly in complex environments like intersections, and prevents the simulation of perception-related errors such as near-accidents. To resolve this, the study proposes a new computational model of driver perception that integrates psychological principles of limited attentional resources, visual attention, and both top-down (goal-driven) and bottom-up (data-driven) information processing. The proposed model treats driving as a multi-task activity split into seven specific subtasks: Lane Keeping, Car Following, Lane Changing, Crossroads Passing, Itinerary Control, Local Rules detection, and Hazard Detection. These subtasks are categorized as either mandatory (always active) or optional (activated based on environmental cues). The core mechanism involves a competition among "percepts" (perceived entities) for access to short-term memory, which serves as the input for the decision module. Due to bounded perceptive resources, only the most subjectively valuable percepts—determined by a balance of top-down expectations and bottom-up salience—are transmitted to the decision module. This architecture is implemented using an agent-based modeling approach within a traffic simulation environment, allowing simulated drivers to manage longitudinal space and conflicts at crossroads realistically. The authors illustrate the model’s benefits through two specific simulation scenarios at crossroads. The first scenario explores traffic parameters by simulating various distributions of driver ages, demonstrating how perceptual limitations vary with demographic factors. The second scenario demonstrates the adaptive behavior of simulated drivers when interacting with a dangerous or hypo-vigilant driver. By incorporating these perceptual limits, the model successfully reproduces realistic interactions and conflict management within intersections, which are often poorly handled by standard microscopic simulations that ignore the inner intersection dynamics. The significance of this work lies in its potential to improve the realism and utility of microscopic traffic simulations. By accounting for human perceptual constraints, the model enables more accurate road safety studies, particularly regarding intersection behavior and the identification of near-accidents caused by perception failures. Furthermore, the approach provides a framework for understanding how individual perceptual limitations contribute to emerging traffic flows, offering a more psychologically grounded alternative to current robotic or idealized driver models. This advancement supports better transport studies and virtual reality applications by ensuring simulated traffic participants behave with credible, human-like limitations.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-20 |
| 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 | 2026-06-20 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-20 |
| verify | partial | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified_with_issues.
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