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Updated: Feb 9, 2026

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高级自动驾驶标准:基于主动生成危险场景的自动驾驶开放循环测试框架
Cheng Wang1, Qiang Liu1, Wenbo Fang1
1Guangdong Provincial Key Laboratory of Intelligent Transportation System, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
Accident; analysis and prevention
|February 7, 2026
概括
自动驾驶需要更好的安全测试. 我们的危险情景主动生成 (HSPG) 框架创建现实的,高风险的驾驶场景,以提高自动驾驶汽车的安全性和性能.
科学领域:
- 计算机科学 计算机科学
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 自动驾驶系统面临着在现实数据中覆盖罕见的高风险场景的挑战.
- 目前的场景生成方法缺乏现实的感知,专注于简单的轨迹扰动.
研究的目的:
- 引入一个主动的框架来生成危险的驾驶场景,以提高自动驾驶的安全性.
- 解决在复杂环境中测试自动驾驶汽车的关键数据的短缺问题.
主要方法:
- 使用自然驾驶数据开发了危险场景主动生成 (HSPG) 框架.
- 使用滑动窗风险指数和高风险车辆检测来识别关键相互作用.
- 采用线性二次调节器 (LQR) 与反复后方策略优化 (RPPO) 以及用于轨迹生成的对抗策略.
- 集成图像合成与现实世界地图数据,以创建现实的街道场景.
主要成果:
- 在测试模型中,HSPG有效地识别了潜在的风险,并将碰撞的可能性放大了超过一个数量级.
- 该框架在各种交通场景中展示了概括性.
- 创建了一个包含150个场景,6019个样本和多视角视图的综合数据集.
结论:
- 该HSPG框架提供了一种可靠的方法,用于生成自动驾驶的安全关键测试数据.
- 创建的数据集可以作为评估和改善自动驾驶系统安全性的宝贵基准.
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