Compensation for Delay in the Visual Display of a Driving Simulator
DOI: 10.1177/003754979706900103
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Summary
This study investigates the effectiveness of a delay compensation technique in the visual display of a driving simulator, specifically examining its impact on lane-keeping performance. Interactive driving simulators using Computer Generated Images (CGI) inherently suffer from visual feedback delays, typically ranging from 50 to 100 milliseconds. This latency can negatively affect driver-vehicle system dynamics, impair task performance, and contribute to simulator-induced sickness. The research aims to validate a previously developed compensation algorithm, which predicts the vehicle’s future position based on current speed and acceleration to offset the display dead time, thereby making the simulator’s behavior more closely resemble that of a real vehicle. The experiment was conducted using the TNO Institute for Perception driving simulator, equipped with an Evans & Sutherland ESIG 2000 image generating system. Twenty-nine male subjects, comprising 17 experienced drivers and 12 inexperienced individuals, participated. The driving task required subjects to maintain the vehicle in the center of a straight road at a constant speed of 80 km/h while compensating for an alternating "side wind" disturbance imposed on the yaw angle. Each subject completed six 90-second runs, with the compensation condition alternating between runs in a balanced order. Data collected at 10 Hz included lateral position, yaw angle, steering wheel angle, and lateral acceleration. Key metrics analyzed were the standard deviations of these variables and the steering wheel reversal rate (SRR), a proxy for steering effort. Results demonstrated that delay compensation had a significant stabilizing effect on the driver-simulator system. The use of compensation significantly reduced the standard deviations of lateral speed, lateral acceleration, yaw angle, and steering wheel angle, as well as the SRR. The average effect size across these variables was approximately 10%, indicating smoother vehicle motion and reduced steering effort. While the reduction in lateral position standard deviation showed a trend toward improvement, it narrowly missed statistical significance. Notably, there was no interaction between driver experience and compensation, indicating that both experienced and inexperienced drivers benefited equally from the technique. Subjects generally did not consciously perceive the difference between compensated and uncompensated runs, despite the measurable improvements in performance metrics. The study concludes that visual display delay compensation is an effective and efficient measure for enhancing driving simulator fidelity. By mitigating the destabilizing effects of visual latency, the compensation algorithm allows the simulator to behave according to the underlying vehicle model, which is designed to approximate real-world vehicle dynamics. This finding supports the implementation of such compensation techniques in research and training simulators to improve validity and reduce potential negative impacts on user performance and comfort.
Key finding
Implementing delay compensation in the driving simulator's visual display significantly smoothed vehicle motion and reduced steering effort for both experienced and inexperienced drivers.
Methodology
simulator
Sample size: 29
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 | 2026-06-06 |
| archive | success | canonical_url | — | — | 1 | 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 |
| promote | success | — | — | — | 1 | 2026-06-06 |
| 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.
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Information type
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- Empirical Findings: behavioral performance data
- Methodological Resource: tool software
- Theoretical Contribution: computational model