The usage of environment sensor models for virtual testing is a promising approach to reduce the testing effort of autonomous driving. However, in order to deduce any statements regarding the performance of an autonomous driving function based on simulation, the sensor model has to be validated to determine the discrepancy between the synthetic and real sensor data. Since a certain degree of divergence can be assumed to exist, the sufficient level of fidelity must be determined, which poses a major challenge. In particular, a method for quantifying the fidelity of a sensor model does not exist and the problem of defining an appropriate metric remains. In this work, we train a neural network to distinguish real and simulated radar sensor data with the purpose of learning the latent features of real radar point clouds. Furthermore, we propose the classifier's confidence score for the `real radar point cloud' class as a metric to determine the degree of fidelity of synthetically generated radar data. The presented approach is evaluated and it can be demonstrated that the proposed deep evaluation metric outperforms conventional metrics in terms of its capability to identify characteristic differences between real and simulated radar data.

翻译：使用环境传感器模型进行虚拟测试是减少自动驾驶测试努力的一个大有希望的办法,但是,为了推断出任何关于基于模拟的自主驱动功能的性能的说明,必须验证传感器模型,以确定合成和真实传感器数据之间的差异;由于可以假定存在某种程度的差异,必须确定足够的忠诚度,这构成重大挑战;特别是,没有一种量化传感器模型的忠诚度的方法,确定适当计量标准的问题仍然存在;在这项工作中,我们训练一个神经网络,以区分真实和模拟雷达传感器数据,目的是了解真实雷达点云的潜在特征;此外,我们提议将“实际雷达点云”等级的分类者信任度作为确定合成生成雷达数据准确度的一种衡量标准;对所提出的方法进行评价,并可以证明,拟议的深评价指标在确定真实和模拟雷达数据之间特征差异的能力方面,超出了常规指标。