基于安全深度增强学习的自适应式巡航控制
Rui Zhao1, Kui Wang2, Wenbo Che1
1College of Automotive Engineering, Jilin University, Changchun 130025, China.
Sensors (Basel, Switzerland)
|April 27, 2024
概括
本研究介绍了安全优先强化学习自适应巡航控制 (SFRL-ACC),这是一种使用深度强化学习进行高效和安全驾驶的新型系统. 在计算时间,交通效率,驾驶舒适性和安全性方面,SFRL-ACC的性能优于传统方法.
科学领域:
- 汽车工程 汽车工程
- 人工智能的人工智能
- 控制系统 控制系统
背景情况:
- 目前的自适应巡航控制 (ACC) 方法面临着模拟困难和计算效率的挑战.
- 基于优化控制的ACC系统往往难以平衡性能与安全性和舒适性.
研究的目的:
- 提出一个新的自适应巡航控制系统,安全第一强化学习自适应巡航控制 (SFRL-ACC),利用深度强化学习 (DRL).
- 通过提高计算效率,安全性和驾驶舒适性来克服现有的ACC方法的局限性.
主要方法:
- 使用受约束的马尔科夫决策过程 (CMDP) 制定了ACC问题作为安全的DRL问题.
- 开发了基于预测受约束政策优化 (PCPO) 的 ACC 算法 (SFRL-ACC) 来解决CMDP.
- 将安全约束纳入DRL政策更新中,使用Kullback-Leibler (KL) 差异信任区域.
主要成果:
- 与基于最先进的模型预测控制 (MPC) 的ACC方法相比,SFRL-ACC系统表现出更高的性能.
- 实验结果显示,计算时间,交通效率,驾驶舒适度和安全性都有所改善.
- 在遵守安全成本限制的同时,PCPO算法有效地最大限度地提高了性能.
结论:
- SFRL-ACC为当前的ACC系统提供了一个更高效,更安全,更舒适的替代方案.
- 提出的安全DRL方法有效地解决了ACC控制的复杂性.
- 这项研究强调了DLR在开发先进汽车控制系统方面的潜力.
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