用强化学习优化匝道并入，提升安全与舒适性。
PPO-Based Vehicle Control for Ramp Merging Scheme Assisted by Enhanced C-V2X
- 基于PPO的强化学习控制车辆动态调整速度和位置
- 增强版C-V2X模式4降低信息老化时间，通信更可靠
- 适合自动驾驶并入场景研究者参考
匝道并入是自动驾驶中的关键挑战，合并车道车辆需动态调整位置与速度，并监测主路交通以避免碰撞。本文提出一种基于强化学习的新型并入控制方案，融合横向控制机制，确保合并车道车辆平稳接入主路，同时优化燃油效率与乘员舒适性。考虑到车对车（V2V）通信对控制策略的影响，引入基于蜂窝车联网（C-V2X）Mode 4的增强协议，旨在降低信息老化时间（Age of Information, AoI）并提升通信可靠性。通过NS3网络仿真器与Python结合，实现V2V通信与车辆控制的协同仿真。采用两种基于AoI的指标评估协议性能。结果表明，增强版C-V2X Mode 4优于标准版本，所提控制方案可保障匝道并入过程的安全与稳定。
原文摘要 · Abstract (English)
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.
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