提出测量自动驾驶车辆远程操控中动作延迟的新方法
Motion-to-Motion Latency Measurement Framework for Connected and Autonomous Vehicle Teleoperation
- 用霍尔传感器与同步树莓派记录操作端与车辆端的中断时间戳
- 实测动作延迟中位数超750毫秒，主要由执行器延迟导致
- 不依赖具体系统架构，可通用评估远程操控延迟
远程操控连通与自动驾驶车辆（CAV）的延迟是关键性能指标，影响操作员对驾驶环境变化的感知和纠正速度。现有研究多聚焦于玻璃到玻璃（G2G）延迟，仅涵盖视频传输延迟。然而，动作到动作（M2M）延迟——即远程操作者物理转向动作与车辆相应转向运动之间的延迟——尚无标准测量方法。本文提出一种M2M延迟测量框架，采用霍尔效应传感器与两台同步的Raspberry Pi 5设备，通过双端中断时间戳记录估算延迟，独立于底层遥操作系统架构。精度测试显示误差在10–15毫秒之间；实地测试表明，执行器延迟主导了整体延迟，中位数超过750毫秒。
原文摘要 · Abstract (English)
Latency is a key performance factor for the teleoperation of Connected and Autonomous Vehicles (CAVs). It affects how quickly an operator can perceive changes in the driving environment and apply corrective actions. Most existing work focuses on Glass-to-Glass (G2G) latency, which captures delays only in the video pipeline. However, there is no standard method for measuring Motion-to-Motion (M2M) latency, defined as the delay between the physical steering movement of the remote operator and the corresponding steering motion in the vehicle. This paper presents an M2M latency measurement framework that uses Hall-effect sensors and two synchronized Raspberry Pi~5 devices. The system records interrupt-based timestamps on both sides to estimate M2M latency, independently of the underlying teleoperation architecture. Precision tests show an accuracy of 10--15~ms, while field results indicate that actuator delays dominate M2M latency, with median values above 750~ms.
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