ACCESS-AV: Adaptive Communication-Computation Codesign for Sustainable Autonomous Vehicle Localization in Smart Factories

2025-07-27

AI Summary

To address the computationally intensive, energy-hungry, and infrastructure-dependent nature of localization modules for Autonomous Delivery Vehicles (ADVs) in 5G-enabled smart factories—particularly their reliance on dedicated Roadside Units (RSUs) or additional onboard sensors—this paper proposes a communication-computation co-design energy-efficient localization framework. Innovatively leveraging 5G Synchronization Signal Blocks (SSBs) for Angle-of-Arrival (AoA) estimation, the framework employs a lightweight MUSIC algorithm coupled with an environment-adaptive resource scheduling strategy to dynamically optimize energy efficiency without compromising localization accuracy. Crucially, it eliminates the need for RSU deployment or supplementary vehicle-mounted sensors, significantly reducing infrastructure and operational costs. Experimental results demonstrate an average energy reduction of 43.09% over conventional approaches, while maintaining sub-30 cm localization error—making it highly suitable for resource-constrained, sustainable smart factory deployments.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Local Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Autonomous Delivery Vehicles (ADVs) are increasingly used for transporting goods in 5G network-enabled smart factories, with the compute-intensive localization module presenting a significant opportunity for optimization. We propose ACCESS-AV, an energy-efficient Vehicle-to-Infrastructure (V2I) localization framework that leverages existing 5G infrastructure in smart factory environments. By opportunistically accessing the periodically broadcast 5G Synchronization Signal Blocks (SSBs) for localization, ACCESS-AV obviates the need for dedicated Roadside Units (RSUs) or additional onboard sensors to achieve energy efficiency as well as cost reduction. We implement an Angle-of-Arrival (AoA)-based estimation method using the Multiple Signal Classification (MUSIC) algorithm, optimized for resource-constrained ADV platforms through an adaptive communication-computation strategy that dynamically balances energy consumption with localization accuracy based on environmental conditions such as Signal-to-Noise Ratio (SNR) and vehicle velocity. Experimental results demonstrate that ACCESS-AV achieves an average energy reduction of 43.09% compared to non-adaptive systems employing AoA algorithms such as vanilla MUSIC, ESPRIT, and Root-MUSIC. It maintains sub-30 cm localization accuracy while also delivering substantial reductions in infrastructure and operational costs, establishing its viability for sustainable smart factory environments.

Problem

• Optimize energy-efficient ADV localization in smart factories
• Reduce costs by using existing 5G infrastructure
• Balance energy consumption with localization accuracy dynamically

Innovation

• Uses 5G SSBs for localization without RSUs
• AoA-based MUSIC algorithm with adaptive strategy
• Reduces energy by 43% with sub-30cm accuracy