CoRTSG:一个一般和有效的风险测试场景生成框架,用于混合交通中的合作感知
Rongsong Li1, Xin Pei2, Lu Xing3
1Department of Automation, BNRist, Tsinghua University, Beijing, China; PowerChina Guiyang Engineering Co., Ltd, Guiyang 550081, China.
Accident; analysis and prevention
|July 13, 2025
概括
合作感知 (COOP) 通过使用V2X通信来克服感知限制,提高自动驾驶汽车的安全性. 本研究引入了一个框架,用于生成风险测试场景,验证COOP.
科学领域:
- 自主驾驶系统 自主驾驶系统
- 智能运输系统 智能运输系统
- 计算机视觉 计算机视觉
背景情况:
- 个别车辆的感知面临着阻塞和远距离可视性的限制.
- 合作感知 (COOP) 利用V2X通信来扩大连接和自动化车辆 (CAV) 的传感能力.
- 现有的研究缺乏对COOP系统的强有力的评估和测试方法,特别是关于驾驶安全.
研究的目的:
- 为COOP系统提出一个通用和有效的框架 (CoRTSG),用于生成风险测试场景.
- 通过从现实世界与封闭相关的崩中提取功能场景来定义COOP的操作设计领域.
- 创建一个全面的库,多样化和具有挑战性的测试场景,用于COOP评估.
主要方法:
- 开发了合作感知风险测试情景生成 (CoRTSG) 框架.
- 集成的交通数据和事先的知识,可以顺序生成功能,逻辑和具体的场景.
- 使用OpenDRIVE地图和快速封闭判断算法,并使用自回归采样来创建场景.
- 在CARLA中模拟了17490个具体场景,用于测试COOP算法.
主要成果:
- 为COOP创建了一个大型库,包含11个功能场景和17490个具体场景.
- 与个人感知相比,COOP显著提高了驾驶安全和检测准确度.
- 确定了优化需求,以平衡性能,带宽和稳定的安全改进.
结论:
- CoRTSG框架为COOP系统生成关键测试场景提供了一种新的方法.
- 在混合交通环境中,COOP对驾驶安全和感知有显著的好处.
- 需要进一步的研究,以优化COOP的实际,广泛部署,解决通信效率和一致的安全收益.
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