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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., Comment: Machine Learning in Certified Systems (MLCS) Workshop, 14.-15.01.2021

Averaging predictions of a deep ensemble of networks is apopular and effective method to improve predictive performance andcalibration in various benchmarks and Kaggle competitions. However, theruntime and training cost of deep ensembles grow linearly with the size ofthe ensemble, making them unsuitable for many applications. Averagingensemble weights instead of predictions circumvents this disadvantageduring inference and is typically applied to intermediate checkpoints ofa model to reduce training cost. Albeit effective, only few works haveimproved the understanding and the performance of weight averaging.Here, we revisit this approach and show that a simple weight fusion (WF)strategy can lead to a significantly improved predictive performance andcalibration. We describe what prerequisites the weights must meet interms of weight space, functional space and loss. Furthermore, we presenta new test method (called oracle test) to measure the functional spacebetween weights. We demonstrate the versatility of our WF strategy acrossstate of the art segmentation CNNs and Transformers as well as real worlddatasets such as BDD100K and Cityscapes. We compare WF with similarapproaches and show our superiority for in- and out-of-distribution datain terms of predictive performance and calibration.