量化自动驾驶系统中的学习算法不确定性:通过多项式混沌扩展和高清地图提高安全性
Ruihe Zhang1, Chen Sun2, Minghao Ning1
1Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo ON, N2L 3G1, Canada.
Accident; analysis and prevention
|December 29, 2024
概括
通过量化算法不确定性,可以提高自动驾驶系统 (ADS) 的安全性. 一种新的多项式混沌扩展 (PCE) 方法准确地测量位置不确定性,并适应不断变化的条件,增强公众的信任.
科学领域:
- 机器人和人工智能 机器人和人工智能
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 自动驾驶系统 (ADS) 承诺通过减少人类错误来提高交通安全.
- 在复杂,动态环境中量化性能不确定性是ADS安全验证的关键挑战.
- 在ADS中学习算法的黑盒性质使安全评估和公众信任复杂化.
研究的目的:
- 引入一种新的多项式混沌扩展 (PCE) 方法来量化ADS对象检测中的位置不确定性.
- 允许在线自动更新不确定性量化,以适应不断变化的运营条件.
- 提高自动驾驶系统的可靠性和可信度.
主要方法:
- 利用多项式混沌扩展 (PCE) 来建模和量化ADS对象检测中的位置不确定性.
- 综合高清地图 (HD) 为不确定性分析提供准确的空间背景.
- 在PCE框架内开发了在线自动更新机制,以处理数据转移.
- 通过模拟和现实驾驶实验验验证了PCE方法.
主要成果:
- 与基线模型相比,PCE方法在不确定性量化方面表现出更高的准确性.
- PCE方法的自我更新能力在适应不断变化的环境条件,如天气等方面被证明是有效的.
- 准确的不确定性量化对于可靠的ADS安全评估至关重要.
结论:
- 拟议的PCE方法为量化ADS对象检测中的不确定性提供了可靠的解决方案.
- 自动更新功能提高了ADS在现实,动态环境中的适应性和可靠性.
- 这项工作有助于建立公众信任,并促进自动驾驶技术的广泛采用.
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