基于数据驱动的风险量化模型的自动驾驶安全的自我进化算法
Shuo Yang1, Shizhen Li1, Yanjun Huang2
1School of Automotive studies, Tongji University, Shanghai, 201804, China.
Accident; analysis and prevention
|February 15, 2025
概括
这项研究介绍了自动驾驶的安全自我进化算法. 它通过量化风险和整合可调节的安全极限来提高复杂交通中的安全性,确保在不牺牲性能的情况下安全勘探.
科学领域:
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
- 计算机科学 计算机科学
背景情况:
- 自动驾驶系统旨在在复杂环境中实现独立进化.
- 在动态交通中在勘探过程中确保安全是具有挑战性的,因为进化算法的安全性能权衡.
研究的目的:
- 为自动驾驶提出一个安全的自我进化算法.
- 为应对在动态交通场景中不损害性能的安全勘探的挑战.
主要方法:
- 开发了一个基于数据的风险量化模型,使用注意力机制,模仿人类的风险感知.
- 将该模型集成到具有可调节安全限制的安全进化决策控制算法中.
主要成果:
- 拟议的算法有效地估计了周围的环境风险.
- 通过模拟和真实车辆实验,在复杂场景中证明了安全和合理的行动生成.
结论:
- 这种新的算法保证了自动驾驶系统的安全.
- 保持基于学习的系统的进化潜力,同时确保安全运行.
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