具有高风险的AV-VRU相互作用的代探索的自适应风险逆转.
Yujia Zhao1, Ying Ni1, Jialin Fan1
1Department of Traffic Engineering & The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai, China.
Accident; analysis and prevention
|January 24, 2026
概括
通过模拟易受伤害的道路使用者 (VRU) 行为,ARISE为自动化车辆 (AV) 安全测试生成了具有挑战性的场景. 这种适应性方法增强了复杂,高风险的相互作用的AV验证.
科学领域:
- 汽车安全工程 汽车安全工程
- 交通运输中的人工智能
- 人与机器人的交互
背景情况:
- 自动化车辆 (AV) 安全验证需要现实的高风险场景,涉及易受伤害的道路使用者 (VRU).
- 目前的方法很难捕捉VRU的行为复杂性,并探索罕见的,关键的长尾风险.
- 这种差距限制了AV安全评估的严谨性和代表性.
研究的目的:
- 引入ARISE (适应性风险逆转方案探索),这是一个创新的框架,用于生成长尾,高风险的AV-VRU交互场景.
- 系统地识别,参数化和完善场景空间中的高风险地区.
- 改善复杂相互作用中AVs的安全验证.
主要方法:
- ARISE集成了以失败驱动的反与可解释的行为建模.
- 一个多维VRU行为模型捕捉了决策的变化.
- 该框架在OnSite模拟环境中实现,用于代的场景生成.
主要成果:
- 与传统方法相比,ARISE显著提高了产生的高风险AV-VRU相互作用的多样性和严重性.
- 该研究确定了具有明确可解释性的关键风险间隔.
- 该框架有助于系统评估AV安全性能.
结论:
- 在具有挑战性的VRU交互中,ARISE为AV安全验证提供了坚实的方法基础.
- 该框架有效地弥合了场景生成和安全评估之间的差距.
- 这种方法对于确保与VRU一起运行的AV的安全至关重要.
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