研究视觉遥操作在延迟下的非线性失稳，发现150-225毫秒延迟导致控制失效。
Nonlinear Performance Degradation of Vision-Based Teleoperation under Network Latency
- 构建可精确注入延迟的测试平台LAVT，实现分布式单向延迟测量。
- 延迟超过150毫秒时，路径完成率从100%骤降至不足50%。
- 视觉延迟与控制延迟叠加会加速系统崩溃，适合自动驾驶安全研究者。
遥操作正日益成为自动驾驶车辆的关键备用方案。然而，网络延迟对基于视觉、以感知驱动的控制影响仍缺乏充分研究。本文研究了在不同网络延迟下，基于摄像头的车道保持闭环系统的稳定性非线性退化。为此，我们开发了面向研究的延迟感知视觉遥操作测试平台（Latency-Aware Vision Teleoperation testbed, LAVT），一个基于ROS 2的框架，支持精确的分布式单向延迟测量与可复现的延迟注入。利用LAVT，在多种道路几何结构下进行了180次仿真闭环实验。结果表明，当单向感知延迟在150毫秒至225毫秒之间时，系统稳定性急剧下降，路径完成率由100%降至50%以下，同时出现振荡不稳和相位滞后现象。进一步发现，额外的控制通道延迟会加剧这一效应，即使视觉延迟恒定，系统失败也会显著提前。通过结合系统的实证分析与LAVT平台，本工作为感知驱动的不稳定性提供了量化洞察，并建立了未来延迟补偿与预测控制策略的可复现基准。项目页面、补充视频与代码见https://bimilab.github.io/paper-LAVT。
原文摘要 · Abstract (English)
Teleoperation is increasingly being adopted as a critical fallback for autonomous vehicles. However, the impact of network latency on vision-based, perception-driven control remains insufficiently studied. The present work investigates the nonlinear degradation of closed-loop stability in camera-based lane keeping under varying network delays. To conduct this study, we developed the Latency-Aware Vision Teleoperation testbed (LAVT), a research-oriented ROS 2 framework that enables precise, distributed one-way latency measurement and reproducible delay injection. Using LAVT, we performed 180 closed-loop experiments in simulation across diverse road geometries. Our findings reveal a sharp collapse in stability between 150 ms and 225 ms of one-way perception latency, where route completion rates drop from 100% to below 50% as oscillatory instability and phase-lag effects emerge. We further demonstrate that additional control-channel delay compounds these effects, significantly accelerating system failure even under constant visual latency. By combining this systematic empirical characterization with the LAVT testbed, this work provides quantitative insights into perception-driven instability and establishes a reproducible baseline for future latency-compensation and predictive control strategies. Project page, supplementary video, and code are available at https://bimilab.github.io/paper-LAVT
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