发表机构
University of California, Santa Barbara（加州大学圣塔芭芭拉分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对动态毫米波信道环境中自主车辆的安全导航与数据传输需求，提出结合控制障碍函数的非线性模型预测控制框架，可降低总能耗最多37.3%。
AI 中文摘要
本文研究自主车辆（AV）在存在移动障碍物的动态环境中、通过毫米波（mmWave）信道通信时的高能效运行问题。障碍物会导致毫米波信道严重衰减，形成高度动态的通信环境。在此场景下，我们考虑联合优化AV的运动与通信能耗问题：AV需在动态障碍物间安全导航至指定目的地，同时确保通过毫米波信道及时传输车载传感或遥测数据。我们寻求一种实时方法，用于在动态障碍物同时引入安全约束和时变毫米波阻塞的场景中计算高能效轨迹，该场景会产生紧密耦合的运动-通信权衡。我们提出非线性模型预测控制（NMPC）框架，该框架支持前瞻性通信与运动决策及能耗协同优化，并辅以控制障碍函数（CBF）以保障安全。大量仿真结果表明，与基线策略相比，所提方法可将总能耗降低多达37.3%。总体而言，我们的结果证明，所提基于NMPC的框架在动态、阻塞敏感的毫米波通信约束下，显著提升了AV的能效与性能。
英文摘要
This paper studies energy-efficient operation of autonomous vehicles (AVs) in dynamic environments with moving obstacles and while communicating over mmWave channels. The obstacles induce severe attenuation of the mmWave channel resulting in a highly dynamic communication environment. In this setting, we consider the problem of jointly optimizing motion and communication energy for an AV that safely navigates among dynamic obstacles toward a designated destination while ensuring timely transmission of onboard sensing or telemetry data over mmWave channels. We then seek a real-time methodology to compute energy-efficient trajectories in a setting where dynamic obstacles induce both safety constraints and time-varying mmWave blockage, leading to tightly coupled motion-communication trade-offs. We propose a nonlinear model predictive control (NMPC) framework that enables anticipative communication and motion decision-making and energy co-optimization, augmented with a control barrier function (CBF) to ensure safety. Extensive simulation results demonstrate the effectiveness of our approach, reducing total energy consumption by up to 37.3% compared to baseline strategies. Overall, our results demonstrate that the proposed NMPC-based framework significantly enhances energy efficiency and performance of AVs under dynamic, blockage-sensitive mmWave communication constraints.
