研究远程驾驶中延迟与画质对操作负荷的影响,发现两者可独立优化。
Driving Through the Network: Performance and Workload Under Latency and Video Impairment

- 通过25人实验操控延迟和码率,测量多维度表现与生理反应。
- 300毫秒延迟+2000kbps画质可达到最佳表现,500kbps则不行。
- 生理指标能提前预警操作过载,适合安全系统设计参考。
远程操控有望拓展自动驾驶车辆的作业范围,但其效果高度依赖网络延迟与视频质量。本研究开展固定基座驾驶模拟实验(N=25),采用2×2因子设计:添加延迟(100/300毫秒)与码率(500/2000千比特/秒),并设置最优基准条件(0毫秒延迟,9000千比特/秒)。测量各条件下有效玻璃到玻璃(G2G)延迟(基准约413毫秒,实际总延迟约500–700毫秒),确认帧率与编码设置稳定。综合评估性能(速度、转向反转、碰撞)、眼动行为(眨眼率、注视时长)、生理指标(心率变异性、心率、皮肤电导)及主观工作量。延迟与码率均增加操作负荷,轻微影响性能;生理指标呈现非加性交互作用,而性能与眼动交互效应较小或不显著。等效性检验显示,300毫秒延迟搭配2000千比特/秒可实现速度等效于最优条件(误差窗±2公里/小时),而500千比特/秒则不能。研究主张将延迟与视频质量作为独立的设计参数,并提出基于生理信号的自适应机制可在安全受损前预判过载。
原文摘要 · Abstract (English)
Teleoperation promises to extend the operational envelope of automated vehicles, yet it critically depends on network latency and video quality. We report a fixed-base driving-simulator study (N=25) with a 2x2 manipulation of added latency (100/300 ms) and bitrate (500/2000 kbit/s), plus a best-case baseline (0 ms added, 9000 kbit/s). We measured effective glass-to-glass (G2G) latency per condition (baseline approx. 413 ms; effective totals approx. 500-700 ms) and verified stable framerate and encoder settings. Multimodal measures covered performance (speed, steering reversals, crashes), oculomotor behavior (blink rate, fixation duration), physiology (RR interval, heart rate, skin conductance), and subjective workload. Latency and bitrate each increased operator load and modestly affected performance. Physiological measures (heart rate, RR interval) exhibited sub-additive interactions, whereas performance and oculomotor interactions were small or non-significant. Equivalence tests showed that 300 ms with 2000 kbit/s was velocity-equivalent to best-case (SESOI +/- 2 km/h), while 300 ms with 500 kbit/s was not. We argue that latency and video quality should be treated as largely independent design levers, and that physiology-aware adaptation can anticipate overload before safety is compromised.
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