Deep learning (DL) has recently attracted increasing interest to improve object type classification for automotive radar.In addition to high accuracy, it is crucial for decision making in autonomous vehicles to evaluate the reliability of the predictions; however, decisions of DL networks are non-transparent. Current DL research has investigated how uncertainties of predictions can be quantified, and in this article, we evaluate the potential of these methods for safe, automotive radar perception. In particular we evaluate how uncertainty quantification can support radar perception under (1) domain shift, (2) corruptions of input signals, and (3) in the presence of unknown objects. We find that in agreement with phenomena observed in the literature,deep radar classifiers are overly confident, even in their wrong predictions. This raises concerns about the use of the confidence values for decision making under uncertainty, as the model fails to notify when it cannot handle an unknown situation. Accurate confidence values would allow optimal integration of multiple information sources, e.g. via sensor fusion. We show that by applying state-of-the-art post-hoc uncertainty calibration, the quality of confidence measures can be significantly improved,thereby partially resolving the over-confidence problem. Our investigation shows that further research into training and calibrating DL networks is necessary and offers great potential for safe automotive object classification with radar sensors.

中文翻译：

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基于深度学习的雷达光谱目标分类不确定性研究

深度学习 (DL) 最近吸引了越来越多的兴趣，以改进汽车雷达的对象类型分类。除了高精度之外，评估预测的可靠性对于自动驾驶汽车的决策制定也至关重要；然而，DL 网络的决策是不透明的。当前的深度学习研究调查了如何量化预测的不确定性，在本文中，我们评估了这些方法在安全的汽车雷达感知方面的潜力。特别是，我们评估了不确定性量化如何在 (1) 域偏移、(2) 输入信号损坏和 (3) 存在未知物体的情况下支持雷达感知。我们发现，与文献中观察到的现象一致，深度雷达分类器过于自信，即使他们的预测是错误的。这引起了对在不确定性下使用置信值进行决策的担忧，因为模型在无法处理未知情况时无法通知。准确的置信度值将允许多个信息源的最佳集成，例如通过传感器融合。我们表明，通过应用最先进的事后不确定性校准，可以显着提高置信度量的质量，从而部分解决过度自信的问题。我们的调查表明，对训练和校准深度学习网络的进一步研究是必要的，并且为使用雷达传感器进行安全的汽车物体分类提供了巨大的潜力。准确的置信度值将允许多个信息源的最佳集成，例如通过传感器融合。我们表明，通过应用最先进的事后不确定性校准，可以显着提高置信度量的质量，从而部分解决过度自信的问题。我们的调查表明，对训练和校准深度学习网络的进一步研究是必要的，并且为使用雷达传感器进行安全的汽车物体分类提供了巨大的潜力。准确的置信度值将允许多个信息源的最佳集成，例如通过传感器融合。我们表明，通过应用最先进的事后不确定性校准，可以显着提高置信度量的质量，从而部分解决过度自信的问题。我们的调查表明，对训练和校准深度学习网络的进一步研究是必要的，并且为使用雷达传感器进行安全的汽车物体分类提供了巨大的潜力。