Modelling visual-vestibular integration and behavioural adaptation in the driving simulator
DOI: 10.1016/j.trf.2019.07.018
archive: archived pipeline: cataloged verified
Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)
Summary
This paper addresses the poorly understood mechanisms of visual-vestibular integration and behavioral adaptation in driving simulators, specifically regarding how drivers respond to down-scaled motion cues. While it is established that drivers rely on both vision and vestibular input and adapt to persistent sensory changes, the underlying processes remain unclear. The authors propose a quantitative driver model to explain empirical observations in slalom tasks, where reduced motion cues initially degrade performance and increase steering effort, followed by partial recovery through adaptation. The study also investigates a counterintuitive finding: a local optimum in performance at intermediate motion scaling levels. The methodology employs a driver steering model based on an intermittent control framework, which assumes drivers make discrete steering adjustments based on accumulated evidence of control errors. The model integrates visual and vestibular estimates of vehicle yaw rate using optimal cue integration, weighting inputs by their perceived reliability. To simulate behavioral adaptation, the authors tested several mechanisms: reliability-based cue reweighting, cue rescaling, changes in steering response gain, and changes in the evidence accumulation gain. Simulations were conducted using a linear vehicle model fitted to data from a Jaguar XF, with parameters optimized to minimize a cost function balancing path-tracking accuracy and steering effort. The results demonstrate that the model successfully reproduces key empirical phenomena. It confirms that drivers directly use vestibular information for steering and that motion down-scaling causes a yaw rate underestimation, where drivers behave as if the vehicle is rotating more slowly than it actually is. Crucially, the model reveals that this underestimation can be beneficial for path tracking in slalom tasks, explaining the observed local optimum in performance at moderate scaling levels. Regarding adaptation, the model indicates that behavioral recovery occurs primarily through the down-weighting of vestibular cues and/or increased sensitivity to control errors, rather than through a full compensatory rescaling of vestibular input. The significance of this work lies in providing a mechanistic explanation for simulator driving behaviors, challenging the assumption that drivers simply rescale sensory inputs to match reality. Instead, adaptation involves adjusting the reliance on specific sensory modalities and control parameters. The developed model offers new hypotheses for multisensory integration and serves as a tool for improving motion cueing algorithms in driving simulators and other domains requiring sensorimotor control.
Key finding
The proposed driver model demonstrates that motion down-scaling causes a yaw rate underestimation that can beneficially improve path-tracking performance at certain scaling levels, and that behavioral adaptation occurs through cue reweighting or increased control error sensitivity rather than full compensatory rescaling of vestibular input.
Methodology
simulation_modeling
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.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-05 |
| archive | success | openalex | — | — | 5 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-07 |
| chunk | success | chunk | — | — | 1 | 2026-06-07 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-07 |
| enrich | success | semantic_scholar | — | — | 1 | 2026-06-06 |
| promote | success | — | — | — | 1 | 2026-06-05 |
| 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
- Theoretical Contribution: computational model, theory or model