LEARNING
RiskFlow:快速且保真的安全关键交通场景生成
Qi Lan, Yining Tang, Yu Shen, Yi Zhou, Yuhao Wei, Jie Li, Guofa Li
- 发表年份
- 2026
- 访问权限
- 开放获取
摘要
本文提出RiskFlow框架,用于生成安全关键的多智能体交通场景。通过将轨迹生成建模为动作空间中的传输过程,并采用单次前向传播替代迭代去噪,显著提升了生成速度与保真度。
关键词
safety-critical scenario generationautonomous drivingdiffusion modeltrajectory generationrisk assessment
相关论文
LEARNING
📊 8,465 引用
The Organization of Behavior
D. O. Hebb
2005
LEARNING
📊 7,678 引用
Fractional Brownian Motions, Fractional Noises and Applications
Benoît B. Mandelbrot, John W. Van Ness
1968
LEARNING
开放获取📊 7,484 引用
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Laith Alzubaidi, Jinglan Zhang, Amjad J. Humaidi 等 10 位作者
2021
LEARNING
📊 4,608 引用
A guide to deep learning in healthcare
Andre Esteva, Alexandre Robicquet, Bharath Ramsundar 等 10 位作者
2018