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

François Provost, Faisal Hawlader, Mehdi Testouri, Raphaël Frank

TL;DR

This work addresses the lack of a standard Motion-to-Motion (M2M) latency metric for teleoperated CAVs by introducing a hardware-software framework that uses Hall-effect sensors on both the remote operator and vehicle steering wheels, with two Chrony-synchronized Raspberry Pi 5s to timestamp events. M2M latency is computed as the difference between steering-initiated events at the two ends, with a breakdown of total latency into generation, network, execution, follow-through, and measurement-error components; the framework aims to be independent of the teleoperation stack. Precision tests demonstrate an overall timing accuracy of 10–15 ms, with the dominant error from sensor calibration (≈10 ms) and negligible clock and kernel jitter. Field experiments reveal actuator-dominated latency, with median M2M values exceeding 750 ms across static and dynamic scenarios, and show that synchronization choice (Co-Ref vs Auto) can affect both median latency and variability; the study highlights the need to evaluate M2M separately from G2G in teleoperation performance and informs routes for reducing total latency in real-world systems.

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.

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

TL;DR

This work addresses the lack of a standard Motion-to-Motion (M2M) latency metric for teleoperated CAVs by introducing a hardware-software framework that uses Hall-effect sensors on both the remote operator and vehicle steering wheels, with two Chrony-synchronized Raspberry Pi 5s to timestamp events. M2M latency is computed as the difference between steering-initiated events at the two ends, with a breakdown of total latency into generation, network, execution, follow-through, and measurement-error components; the framework aims to be independent of the teleoperation stack. Precision tests demonstrate an overall timing accuracy of 10–15 ms, with the dominant error from sensor calibration (≈10 ms) and negligible clock and kernel jitter. Field experiments reveal actuator-dominated latency, with median M2M values exceeding 750 ms across static and dynamic scenarios, and show that synchronization choice (Co-Ref vs Auto) can affect both median latency and variability; the study highlights the need to evaluate M2M separately from G2G in teleoperation performance and informs routes for reducing total latency in real-world systems.

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.

Paper Structure

This paper contains 12 sections, 3 equations, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Architecture
  3. Hardware
  4. Software
  5. Preliminary Evaluation
  6. Precision Test
  7. Experiment Setup
  8. Precision Test Results
  9. Field Test
  10. Experiment Setup
  11. Field Test Results
  12. Conclusion and Future Work

Figures (2)

  • Figure 1: Overview of the m2m latency measurement framework. Hall-effect sensors detect steering wheel motion on both sides. Each detection triggers a hardware interrupt that is timestamped on two Raspberry Pi 5 devices synchronized using Chrony.
  • Figure 2: Latency measurements for (a) static scenarios using WiFi and 5G under stationary conditions, and (b) dynamic scenarios comparing co-referenced (Co-Ref) and autonomous (Auto) synchronization while driving at approximately 10 km/h using a 5G connection.