Increasing demands on reliability and safety of automated driving functions require an augmented usage of simulation tools for the efficient calibration of these functions. However, finding an optimal solution can be costly, especially when the objective function is represented by scenario simulations. To face these challenges, a novel optimization scheme for simulation based calibration problems, that enables reduced computational effort is introduced. The approach is based on sensitivity analyses that provide scenario specific influential parameter spaces. Using these information, all parameter combinations are checked for reference candidates obtained in preceding iterations that are expected to have an equivalent solution as the new set. Thus, expensive simulation runs can be replaced by taking results from a reference set. The so called ‘scenario simulation reduction’ approach is applied to the parameterization of an SAE level 3 automated driving function with a genetic algorithm as optimizer. In order to take modeling inaccuracies into account, a robustness analysis with respect to simulation model parameters is conducted. Finally, a validation of the optimization scheme is performed using an extensive sampling approach. Studies confirm that negligible errors occur that are not expected to disturb optimization progress.

中文翻译：

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基于灵敏度优化的基于仿真的自动驾驶功能标定

对自动驾驶功能的可靠性和安全性的要求不断提高，因此需要更多地使用仿真工具来有效校准这些功能。但是，找到最佳解决方案可能会付出高昂的代价，特别是当目标功能由情景模拟表示时。为了面对这些挑战，介绍了一种用于基于仿真的校准问题的新型优化方案，该方案可减少计算量。该方法基于灵敏度分析，该灵敏度分析提供了场景特定的影响参数空间。使用这些信息，检查所有参数组合中是否有在先前迭代中获得的参考候选，这些参考候选预期具有与新集合等效的解决方案。因此，可以通过从参考集中获取结果来代替昂贵的模拟运行。所谓的“场景模拟减少”方法应用于以遗传算法为优化器的SAE 3级自动驾驶功能的参数化。为了考虑建模误差，针对仿真模型参数进行了稳健性分析。最后，使用广泛的采样方法对优化方案进行验证。研究证实，发生的微不足道的错误不会干扰优化进度。使用广泛的采样方法对优化方案进行验证。研究证实，可以忽略的错误不会影响优化进度。使用广泛的采样方法对优化方案进行验证。研究证实，发生的微不足道的错误不会干扰优化进度。