Video Quality Monitoring for Remote Autonomous Vehicle Control

TL;DR

This paper proposes an integrated AI-driven video quality monitoring system for remote autonomous vehicle control over 4G/5G networks, emphasizing real-time quality prediction, adaptive decision-making, and explainability to improve operational reliability.

Contribution

It introduces a comprehensive blueprint combining onboard data collection, AI-based quality prediction, and proactive network adaptation specifically for remote vehicle operation.

Findings

Benchmarking of 20 AI model variants for accuracy and latency.

Analysis of onboard versus edge inference trade-offs.

Discussion on explainable AI to improve transparency.

Abstract

The delivery of high-quality, low-latency video streams is critical for remote autonomous vehicle control, where operators must intervene in real time. However, reliable video delivery over Fourth/Fifth-Generation (4G/5G) mobile networks is challenging due to signal variability, mobility-induced handovers, and transient congestion. In this paper, we present a comprehensive blueprint for an integrated video quality monitoring system, tailored to remote autonomous vehicle operation. Our proposed system includes subsystems for data collection onboard the vehicle, video capture and compression, data transmission to edge servers, real-time streaming data management, Artificial Intelligence (AI) model deployment and inference execution, and proactive decision-making based on predicted video quality. The AI models are trained on a hybrid dataset that combines field-trial measurements with synthetic stress segments and covers Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and encoder-only Transformer architectures. As a proof of concept, we benchmark 20 variants from these model classes together with feed-forward Deep Neural Network (DNN) and linear-regression baselines, reporting accuracy and inference latency. Finally, we study the trade-offs between onboard and edge-based inference. We further discuss the use of explainable AI techniques to enhance transparency and accountability during critical remote-control interventions. Our proactive approach to network adaptation and Quality of Experience (QoE) monitoring aims to enhance remote vehicle operation over next-generation wireless networks.