可学习的操作设计状态监测器用于自动驾驶中的故障预测
Haowei Xu1, Yougang Bian1, Yang Li1
1State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.
Accident; analysis and prevention
|June 28, 2025
概括
自动驾驶汽车事故往往是由于超出了操作设计条件 (ODC) 的结果. 本研究介绍了ODCNet,这是一个通过监控ODC边界来预测故障的新系统,提高了自动驾驶安全.
科学领域:
- * 自主系统 * 自主系统
- * 人工智能 * 人工智能
- * 机器学习 * 机器学习
背景情况:
- * 自动驾驶汽车事故经常源于违反操作设计条件 (ODC).
- *目前的ODC监测依赖于基本逻辑,无法检测复杂的互动风险.
- *积极预测故障对于确保自主系统的安全性和可靠性至关重要.
研究的目的:
- *为可学习的操作设计条件监控器 (ODCNet) 开发一个端到端的框架.
- *通过监测ODC边界状态,能够准确预测自主系统故障.
- *通过先进的ODC监控,提高自动驾驶汽车的安全性和可靠性.
主要方法:
- *开发了ODCNet,这是一个将操作状态投射到潜伏表示的架构.
- *利用神经推理进行概率边界估计和混合高斯过程回归进行错误校准.
- * 实施了自适应式主动学习机制,以持续改进边界精度.
主要成果:
- * ODCNet在案例研究中展示了事故发生前有效的故障预测能力.
- * 该系统成功监控了十字路口,车道保持和车辆检测场景的边界状态.
- * 验证了ODC监控器在不同操作环境中的预测性能.
结论:
- * 拟议的ODCNet框架提供了一个可靠的方法来预测自主系统故障.
- *持续监测和ODC边界的适应性学习对于减轻事故至关重要.
- *这种方法大大提高了自动驾驶汽车的安全性和运行完整性.
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