论文标题
在线V2X安排原始合作感
Online V2X Scheduling for Raw-Level Cooperative Perception
论文作者
论文摘要
当视野限制了独立情报时，对连接车辆的合作感被救出。虽然原始合作感保留了大多数信息以确保准确性，但它在通信带宽和计算能力方面要求。因此，重要的是安排最有益的工具，以补充视图和稳定的网络连接来共享其传感器。在本文中，我们提出了一种原始合作感知的模型，并提出了传感器共享调度的能量最小化问题，作为多武器匪徒（MAB）问题的变体。具体而言，考虑到相邻车辆的波动性，V2X通道的异质性以及随时间变化的交通环境。然后，我们提出了一种基于在线学习的算法，并具有对数性能损失，从而在探索和开发之间取得了不错的权衡。在不同方案下的仿真结果表明，与基线算法相比，所提出的算法很快就会学会安排最佳合作工具并节省更多的能源。
Cooperative perception of connected vehicles comes to the rescue when the field of view restricts stand-alone intelligence. While raw-level cooperative perception preserves most information to guarantee accuracy, it is demanding in communication bandwidth and computation power. Therefore, it is important to schedule the most beneficial vehicle to share its sensor in terms of supplementary view and stable network connection. In this paper, we present a model of raw-level cooperative perception and formulate the energy minimization problem of sensor sharing scheduling as a variant of the Multi-Armed Bandit (MAB) problem. Specifically, volatility of the neighboring vehicles, heterogeneity of V2X channels, and the time-varying traffic context are taken into consideration. Then we propose an online learning-based algorithm with logarithmic performance loss, achieving a decent trade-off between exploration and exploitation. Simulation results under different scenarios indicate that the proposed algorithm quickly learns to schedule the optimal cooperative vehicle and saves more energy as compared to baseline algorithms.
