Safety measures need to be systemically investigated to what extent they evaluate the intended performance of Deep Neural Networks (DNNs) for critical applications. Due to a lack of verification methods for high-dimensional DNNs, a trade-off is needed between accepted performance and handling of out-of-distribution (OOD) samples. This work evaluates rejecting outputs from semantic segmentation DNNs by applying a Mahalanobis distance (MD) based on the most probable class-conditional Gaussian distribution for the predicted class as an OOD score. The evaluation follows three DNNs trained on the Cityscapes dataset and tested on four automotive datasets and finds that classification risk can drastically be reduced at the cost of pixel coverage, even when applied on unseen datasets. The applicability of our findings will support legitimizing safety measures and motivate their usage when arguing for safe usage of DNNs in automotive perception.

翻译：安全措施需要系统地研究其在多大程度上评估了深度神经网络在关键应用中的预期性能。由于高维深度神经网络缺乏验证方法，需要在可接受的性能与处理分布外样本之间进行权衡。本研究通过应用基于预测类别的最可能类条件高斯分布的马氏距离作为分布外得分，评估了从语义分割深度神经网络中拒绝输出的效果。评估基于在Cityscapes数据集上训练的三个深度神经网络，并在四个自动驾驶数据集上进行测试，发现即使在未见过的数据集上，分类风险也能以像素覆盖率为代价大幅降低。我们的研究发现的应用将有助于验证安全措施的合理性，并在论证深度神经网络在自动驾驶感知中的安全使用时激励其应用。