Abstract

Radar is a key sensor to achieve a reliable environment perception for advanced driver assistance system and automated driving (ADAS/AD) functions. Reducing the development efforts for ADAS functions and eventually enabling AD functions demands the extension of conventional physical test drives with simulations in virtual test environments. In such a virtual test environment, the physical radar unit is replaced by a virtual radar model. Driving datasets, such as the nuScenes dataset, containing large amounts of annotated sensor measurements, help understand sensor capabilities and play an important role in sensor modeling. This article includes a thorough analysis of the radar data available in the nuScenes dataset. Radar properties, such as detection thresholds, and detection probabilities depending on object, environment, and radar parameters, as well as object properties, such as reflection behavior depending on object type, are investigated quantitatively. The overall detection probability of the considered radar (Continental ARS-408-21) was found to be 27.81%. Four radar models on object level with different complexity levels and different parametrisation requirements are presented: a simple RCS-based radar model with an accuracy of 51%, a linear SVC model with an accuracy of 70%, a Random Forest model with an accuracy of 83%, and a Gradient Boost model with an accuracy of 86%. The feature importance analysis of the machine learning algorithms revealed that object class, object size, and object visibility are the most important parameters for the presented radar models. In contrast, daytime and weather conditions seem to have only minor influence on the modeling results.

摘要

雷达是为高级驾驶辅助系统和自动驾驶 (ADAS/AD) 功能实现可靠环境感知的关键传感器。减少 ADAS 功能的开发工作并最终启用 AD 功能需要通过虚拟测试环境中的模拟来扩展传统的物理测试驱动器。在这样的虚拟测试环境中，物理雷达单元被虚拟雷达模型所取代。驱动数据集（例如 nuScenes 数据集）包含大量带注释的传感器测量值，有助于了解传感器功能并在传感器建模中发挥重要作用。本文包括对 nuScenes 数据集中可用的雷达数据的全面分析。雷达属性，例如检测阈值，以及取决于对象、环境和雷达参数的检测概率，以及对象属性，例如取决于对象类型的反射行为，进行了定量研究。所考虑的雷达（大陆 ARS-408-21）的总体检测概率为 27.81%。提出了四种不同复杂程度和不同参数化要求的对象级雷达模型：一个简单的基于RCS的雷达模型准确率51%，线性SVC模型准确率70%，随机森林模型准确率83%，梯度提升模型准确率86%。机器学习算法的特征重要性分析表明，对象类别、对象大小和对象可见性是所提出的雷达模型最重要的参数。相比之下，白天和天气条件似乎对建模结果的影响很小。