MO-RF-THz-Highway-env

该数据集是一个增强的模拟框架，旨在支持在多频段车联网中，对多个自动驾驶车辆（AVs）的自动驾驶策略以及5G/6G网络选择的支持。该框架的特点是自动驾驶车辆可变的车速以及随机布置的无线基站（RBSs）和传输基站（TBSs），以测试在不同网络和交通场景下多目标强化学习（MORL）训练的有效性。该数据集的规模包括5个目标自动驾驶车辆和20个周边自动驾驶车辆，在设有五条单向车道的高速公路上进行模拟。任务是通过多目标强化学习优化无线网络选择和车辆运动动力学。

This dataset is an enhanced simulation framework designed to support the evaluation of autonomous driving strategies and 5G/6G network selection for a fleet of autonomous vehicles (AVs) in multi-band vehicular networks. This framework is characterized by variable AV speeds, as well as randomly deployed Radio Base Stations (RBSs) and Transmission Base Stations (TBSs), to validate the effectiveness of multi-objective reinforcement learning (MORL) training across diverse network and traffic scenarios. The simulation setup of this dataset includes 5 target AVs and 20 surrounding AVs, with the test scenario conducted on a highway with five unidirectional lanes. The core task of this framework is to optimize wireless network selection and vehicle motion dynamics via multi-objective reinforcement learning.