论文标题
使用COPULAS对随机数据进行建模，以应用自主驾驶验证
Modeling Stochastic Data Using Copulas For Application in Validation of Autonomous Driving
论文作者
论文摘要
对全自动车辆的验证和验证与在虚拟环境中反映其所有相互作用的现实世界几乎棘手的挑战有关。有影响力的随机参数需要从现实世界的测量和实时数据中提取，以捕获所有相互依存关系，以进行现实的准确模拟。副群是代表多元分布的概率模型，研究了基本变量之间的依赖性。该模型用于来自包含相关随机参数的回旋处的无人机测量数据。借助Copula模型，生成了反映实时数据的样品。讨论和探索结果的应用和可能的扩展。
Verification and validation of fully automated vehicles is linked to an almost intractable challenge of reflecting the real world with all its interactions in a virtual environment. Influential stochastic parameters need to be extracted from real-world measurements and real-time data, capturing all interdependencies, for an accurate simulation of reality. A copula is a probability model that represents a multivariate distribution, examining the dependence between the underlying variables. This model is used on drone measurement data from a roundabout containing dependent stochastic parameters. With the help of the copula model, samples are generated that reflect the real-time data. Resulting applications and possible extensions are discussed and explored.
