ACCESS-AV: Adaptive Communication-Computation Codesign for Sustainable Autonomous Vehicle Localization in Smart Factories
Rajat Bhattacharjya, Arnab Sarkar, Ish Kool, Sabur Baidya, Nikil Dutt
自主交付车辆(ADV)越来越多地用于在支持5G网络的智能工厂中运输货物,计算密集型本地化模块为优化提供了重要机会。 我们提出了ACCESS-AV,这是一种节能的车辆对基础设施(V2I)本地化框架,利用智能工厂环境中现有的5G基础设施。 通过机会性地访问定期广播的5G同步信号块(SSB)进行本地化,ACCESS-AV无需专用路边单元(RSU)或额外的车载传感器来实现能源效率和降低成本。 我们使用多信号分类(MUSIC)算法实现了基于AOA的到达角度(AoA)估计方法,该算法通过自适应通信计算策略针对资源受限的ADV平台进行了优化,该策略根据环境条件(如信号噪声比(SNR)和车辆速度)动态平衡能源消耗与定位精度。 实验结果表明,ACCESS-AV实现了平均能量降低43.09
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 locali...