Motion-to-Motion Latency Measurement Framework for Connected and Autonomous Vehicle Teleoperation

TL;DR

This paper introduces a standardized framework for measuring Motion-to-Motion latency in CAV teleoperation, using Hall-effect sensors and Raspberry Pi devices to accurately capture delays.

Contribution

It provides a novel, architecture-independent method for quantifying M2M latency in CAV teleoperation systems.

Findings

Median M2M latency exceeds 750 ms in field tests.

Actuator delays are the primary contributor to M2M latency.

The framework achieves 10-15 ms measurement accuracy.

Abstract

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.