End-to-End Latency Measurement Methodology for Connected and Autonomous Vehicle Teleoperation

2026-02-19

AI Summary

This study addresses a critical gap in the evaluation of teleoperation systems, where existing approaches predominantly focus on glass-to-glass (G2G) latency while neglecting motion-to-motion (M2M) latency—the delay from control input to vehicle actuation—thus failing to fully characterize end-to-end (E2E) performance. To overcome this limitation, the work proposes a novel E2E measurement framework that integrates both G2G and M2M latency through a synchronized assessment architecture. Using two GPS-synchronized Raspberry Pi 5 units equipped with gyroscopes, phototransistors, and LED-triggered interrupts, the system employs low-pass filtering and threshold detection to precisely identify steering actions and align events across endpoints. Experimental results over commercial 4G/5G networks reveal an average E2E latency of approximately 500 ms (±4 ms), with M2M latency accounting for up to 60% of the total, thereby demonstrating the method’s high precision and validity.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Agent Communication
• Application Domains: Mobility, Driving & Flight

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Connected and Autonomous Vehicles (CAVs) continue to evolve rapidly, and system latency remains one of their most critical performance parameters, particularly when vehicles are operated remotely. Existing latency-assessment methodologies focus predominantly on Glass-to-Glass (G2G) latency, defined as the delay between an event occurring in the operational environment, its capture by a camera, and its subsequent display to the remote operator. However, G2G latency accounts for only one component of the total delay experienced by the driver. The complementary component, Motion-to-Motion (M2M) latency, represents the delay between the initiation of a control input by the remote driver and the corresponding physical actuation by the vehicle. Together, M2M and G2G constitute the overall End-to-End (E2E) latency. This paper introduces a measurement framework capable of quantifying M2M, G2G, and E2E latencies using gyroscopes, a phototransistor, and two GPS-synchronized Raspberry Pi 5 units. The system employs low-pass filtering and threshold-based detection to identify steering-wheel motion on both the remote operator and vehicle sides. An interrupt is generated when the phototransistor detects the activation of an LED positioned within the camera's Field Of View (FOV). Initial measurements obtained from our teleoperated prototype vehicle over commercial 4G and 5G networks indicate an average E2E latency of approximately 500 ms (measurement precision +/- 4 ms). The M2M latency contributes up to 60% of this value.

Problem

• End-to-End Latency
• Connected and Autonomous Vehicles
• Teleoperation
• Motion-to-Motion Latency
• Glass-to-Glass Latency

Innovation

• End-to-End Latency
• Motion-to-Motion Latency
• Teleoperation
• Raspberry Pi 5
• GPS Synchronization