On-ramp merging presents a critical challenge in autonomous driving, as vehicles from merging lanes need to dynamically adjust their positions and speeds while monitoring traffic on the main road to prevent collisions. To address this challenge, we propose a novel merging control scheme based on reinforcement learning, which integrates lateral control mechanisms. This approach ensures the smooth integration of vehicles from the merging lane onto the main road, optimizing both fuel efficiency and passenger comfort. Furthermore, we recognize the impact of vehicle-to-vehicle (V2V) communication on control strategies and introduce an enhanced protocol leveraging Cellular Vehicle-to-Everything (C-V2X) Mode 4. This protocol aims to reduce the Age of Information (AoI) and improve communication reliability. In our simulations, we employ two AoI-based metrics to rigorously assess the protocol's effectiveness in autonomous driving scenarios. By combining the NS3 network simulator with Python, we simulate V2V communication and vehicle control simultaneously. The results demonstrate that the enhanced C-V2X Mode 4 outperforms the standard version, while the proposed control scheme ensures safe and reliable vehicle operation during on-ramp merging.

翻译：匝道汇入是自动驾驶领域的关键挑战，汇入车道车辆需在监测主路交通的同时动态调整自身位置与速度以防止碰撞。为应对这一挑战，我们提出一种融合横向控制机制的强化学习新型汇入控制方案。该方法能确保汇入车道车辆平稳融入主路交通，同时优化燃油效率与乘员舒适度。此外，我们认识到车对车（V2V）通信对控制策略的影响，引入基于蜂窝车联网（C-V2X）模式4的增强协议。该协议旨在降低信息年龄（AoI）并提升通信可靠性。仿真中采用两种基于AoI的度量指标，严格评估该协议在自动驾驶场景中的效能。通过联合NS3网络仿真器与Python，我们同步模拟V2V通信与车辆控制。结果表明：增强型C-V2X模式4性能优于标准版本，而所提控制方案能确保匝道汇入过程中的车辆运行安全可靠。