Autonomous driving (AD) and advanced driver assistance systems (ADAS) increasingly utilize deep neural networks (DNNs) for improved perception or planning. Nevertheless, DNNs are quite brittle when the data distribution during inference deviates from the data distribution during training. This represents a challenge when deploying in partly unknown environments like in the case of ADAS. At the same time, the standard confidence of DNNs remains high even if the classification reliability decreases. This is problematic since following motion control algorithms consider the apparently confident prediction as reliable even though it might be considerably wrong. To reduce this problem real-time capable confidence estimation is required that better aligns with the actual reliability of the DNN classification. Additionally, the need exists for black-box confidence estimation to enable the homogeneous inclusion of externally developed components to an entire system. In this work we explore this use case and introduce the neighborhood confidence (NHC) which estimates the confidence of an arbitrary DNN for classification. The metric can be used for black-box systems since only the top-1 class output is required and does not need access to the gradients, the training dataset or a hold-out validation dataset. Evaluation on different data distributions, including small in-domain distribution shifts, out-of-domain data or adversarial attacks, shows that the NHC performs better or on par with a comparable method for online white-box confidence estimation in low data regimes which is required for real-time capable AD/ADAS.

翻译：自动驾驶（AD）和高级驾驶辅助系统（ADAS）日益采用深度神经网络（DNN）来改进感知或规划。然而，当推理时的数据分布偏离训练时的数据分布时，DNN相当脆弱。这在部署于部分未知环境（如ADAS场景）时构成了挑战。同时，即使分类可靠性下降，DNN的标准置信度仍然保持较高水平。这是有问题的，因为后续运动控制算法会将看似自信的预测视为可靠，尽管它可能相当错误。为缓解此问题，需要能够与DNN分类实际可靠性更好对齐的实时置信度估计。此外，还需要黑盒置信度估计，以实现将外部开发的组件同质化地集成到整个系统中。在这项工作中，我们探讨了这一应用场景，并提出了邻域置信度（NHC），用于估计任意分类DNN的置信度。该指标可用于黑盒系统，因为它仅需top-1类别输出，而无需访问梯度、训练数据集或保留验证数据集。在不同数据分布（包括小的域内分布偏移、域外数据或对抗攻击）上的评估表明，在实现实时AD/ADAS所需的低数据量情况下，NHC的性能优于或等同于用于在线白盒置信度估计的可比方法。