学习辅助观察员设计,以提高自动驾驶汽车的安全性
András Mihály1, Balázs Németh2, Mihály Kopasz2
1Systems and Control Laboratory, Institute for Computer Science and Control, Hungarian Research Network, Kende u. 13-17, Budapest, 1111, Hungary. mihaly.andras@sztaki.hun-ren.hu.
Scientific reports
|March 3, 2026
概括
本研究提出了一种用于自动驾驶汽车的新观察设计,集成基于模型和机器学习的方法. 它通过在关键机动中检测稳定性损失和性能退化来提高安全性和效率.
科学领域:
- 汽车工程 汽车工程
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 在复杂的机动中,自动化车辆的安全性和效率至关重要.
- 现有的稳定性指数方法在检测微妙的性能下降方面存在局限性.
研究的目的:
- 引入一种新的观察者设计,以提高自动驾驶汽车的安全性和效率.
- 开发一种能够检测稳定性损失和性能恶化的方法.
主要方法:
- 利用观察者设计架构评估横向动态状态.
- 基于模型和基于机器学习 (强化学习) 的综合方法.
- 定义了基于改进向量的横向安全指数.
主要成果:
- 新型观察器提供了高效的轨迹跟踪和安全评估.
- 基于强化学习的观察者在非线性动态下实现了小的观察误差.
- 该方法同时识别安全和性能损失的危险.
结论:
- 提出的基于观察者的安全评估方法有效地检测稳定性和性能损失.
- 这种方法提高了自动驾驶汽车在关键机动过程中的可靠性和安全性.
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