AI 中文总结
针对协作感知中因带宽有限致数据过时问题，提出移动感知AoI最小化框架，推导闭式表达式，开发LocMW算法，性能保证其优于基准，仿真显示该算法能大幅降低AoI并提高检测精度。
AI 中文摘要
协作感知（CP）可提高自动驾驶安全性，但有限带宽会导致CP系统中共享数据严重过时。现有信息年龄（AoI）最小化策略不适用于CP，因为忽视了车辆的AoI不仅通过源（基站）更新，还通过车辆本地传感降低。为此，我们提出了一个用于CP的移动感知AoI最小化框架，明确考虑车辆动态传感范围。首先推导了考虑区域内长期时间平均和AoI的闭式表达式，基于此开发了局部感知感知最大权重调度（LocMW）算法。性能保证表明，与最优固定随机基准相比，LocMW实现了次线性累积超额AoI。大量仿真表明，LocMW策略显著优于竞争基线，将时间平均和AoI降低多达31.6%，将mAP检测精度提高多达16.3%。
英文摘要
While collaborative perception (CP) enhances the safety of autonomous driving, limited bandwidth can cause severe shared data staleness in CP systems. Existing age-of-information (AoI) minimization policies are not well-suited for CP, as they overlook the fact that a vehicle's AoI decreases not only through updates from the source (i.e., a base station) but also through the vehicle's local sensing. To address this issue, we propose a mobility-aware AoI minimization framework for CP that explicitly accounts for vehicles' dynamic sensing ranges. We first derive a closed-form expression for the long-term time average sum AoI within a considered region, accommodating an ever-changing vehicle population and their dynamic sensed areas. Based on this characterization, we develop Local-sensing-aware Max-Weight Scheduling (LocMW), an online learning algorithm designed for sensor information broadcast from a source to vehicles under unknown environmental statistics and delayed observations. We provide performance guarantees demonstrating that LocMW achieves a sublinear cumulative excess AoI compared to the optimal stationary randomized benchmark. Extensive simulations using vehicular trajectory datasets and 3D perception tasks demonstrate that our LocMW policy substantially outperforms competing baselines, reducing the time-averaged sum AoI by up to 31.6% and improving mAP detection accuracy by up to 16.3%.
Comments15 pages, 8 figures
