Safeguarding and type approval of automated vehicles is a key enabler for their market launch in our complex traffic environment. Scenario-based testing by means of computer simulation is becoming increasingly important to cope with the enormous complexity and effort. However, there is a huge gap when assessing the safety of the virtual vehicle while the real vehicle will drive on the road. Simulation must be accompanied by model validation to ensure its credibility since errors and uncertainties are inherent in every model. Unfortunately, this is rarely addressed in the current literature. In this paper, a modular process is presented covering both model validation and safeguarding. It is characterized by the fact that it quantifies a large number of errors and uncertainties, represents them in the form of an error model, and ultimately integrates them into the safeguarding results. It is applied to a type-approval regulation for the lane-keeping behavior of a vehicle under various scenario conditions. The paper contains a thorough validation of the methodology itself by comparing its results with actual ground truth values. For this comparison, a binary classifier and confusion matrices are used that relate the binary type-approval decisions. The classifier demonstrates that the methodology of this paper identifies a systematic error of the simulation model across several safeguarding scenarios. Finally, the paper provides recommendations for alternative configurations of the modular methodology depending on different requirements.

在我们复杂的交通环境中，对自动驾驶汽车进行保护和型号批准是其在市场上推广的关键。通过计算机仿真进行基于场景的测试对于应对巨大的复杂性和工作量变得越来越重要。但是，在评估虚拟车辆的安全性时，尽管实际车辆会在道路上行驶，但差距仍然很大。仿真必须伴随模型验证，以确保其可信度，因为每个模型都固有错误和不确定性。不幸的是，这在当前文献中很少涉及。在本文中，提出了涵盖模型验证和保障的模块化过程。它的特征在于，它可以量化大量错误和不确定性，并以错误模型的形式表示它们，最终将它们整合到维护结果中。它适用于各种场景条件下的车辆车道保持行为的类型批准法规。本文通过将其结果与实际的地面真实值进行比较，对方法本身进行了全面的验证。为了进行比较，使用了与二进制类型批准决策相关的二进制分类器和混淆矩阵。分类器表明，本文的方法论可识别跨多个保护方案的仿真模型的系统性错误。最后，本文根据不同的需求为模块化方法的替代配置提供了建议。它适用于各种场景条件下的车辆车道保持行为的类型批准法规。本文通过将其结果与实际的地面真实值进行比较，对方法本身进行了全面的验证。为了进行比较，使用了与二进制类型批准决策相关的二进制分类器和混淆矩阵。分类器表明，本文的方法论可识别跨多个保护方案的仿真模型的系统性错误。最后，本文根据不同的需求为模块化方法的替代配置提供了建议。它适用于各种场景条件下的车辆车道保持行为的类型批准法规。本文通过将其结果与实际的地面真实值进行比较，对方法本身进行了全面的验证。为了进行比较，使用了与二进制类型批准决策相关的二进制分类器和混淆矩阵。分类器表明，本文的方法论可识别跨多个保护方案的仿真模型的系统性错误。最后，本文根据不同的需求为模块化方法的替代配置提供了建议。使用与二进制类型批准决策相关的二进制分类器和混淆矩阵。分类器表明，本文的方法论可识别跨多个保护方案的仿真模型的系统性错误。最后，本文根据不同的需求为模块化方法的替代配置提供了建议。使用与二进制类型批准决策相关的二进制分类器和混淆矩阵。分类器表明，本文的方法论可识别跨多个保护方案的仿真模型的系统性错误。最后，本文根据不同的需求为模块化方法的替代配置提供了建议。