Ensuring safety in automated driving is a major challenge for the automotive industry. Special attention is paid to artificial intelligence, in particular to Deep Neural Networks (DNNs), which is considered a key technology in the realization of highly automated driving. DNNs learn from training data, which means that they only achieve good accuracy within the underlying data distribution of the training data. When leaving the training domain, a distributional shift is caused, which can lead to a drastic reduction of accuracy. In this work, we present a proof of concept for a safety mechanism that can detect the leaving of the domain online, i.e. at runtime. In our experiments with the Synthia data set we can show that a 100 % correct detection of whether the input data is inside or outside the domain is achieved. The ability to detect when the vehicle leaves the domain can be an important requirement for certification.

翻译：保障自动驾驶的安全性已成为汽车工业面临的重大挑战。人工智能尤其是深度神经网络（DNNs）作为实现高度自动驾驶的关键技术受到特别关注。由于DNNs依赖训练数据进行学习，其仅在训练数据的基础数据分布范围内才能保持较高的准确性。当脱离训练域时，将引发数据分布偏移，导致模型准确率急剧下降。本研究提出一种安全机制的概念验证方案，该方案能够在线（即运行时）检测模型是否脱离训练域。在Synthia数据集上的实验表明，该方法实现了对输入数据是否处于域内/域外的100%准确检测。这种对车辆脱离域状态的检测能力，有望成为自动驾驶系统认证的重要技术要求。