Safety Monitoring of Neural Networks Using Unsupervised Feature Learning and Novelty Estimation

Neural networks are currently suggested to be implemented in several different driving functions of autonomous vehicles. While showing promising results the drawback lies in the difficulty of safety verification and ensuring operation as intended. The aim of this paper is to increase safety when using neural networks, by proposing a monitoring framework based on novelty estimation of incoming driving data. The idea is to use unsupervised instance discrimination to learn a similarity measure across ego-vehicle camera images. By estimating a von Mises-Fisher distribution of
expected
ego-camera images they can be compared with
unexpected
novel images. A novelty measurement is inferred through the likelihood of test frames belonging to the expected distribution. The suggested method provides competitive results to several other novelty or anomaly detection algorithms on the CIFAR-10 and CIFAR-100 datasets. It also shows promising results on real world driving scenarios by distinguishing novel driving scenes from the training data of BDD100 k. Applied on the identical training-test data split, the method is also able to predict the performance profile of a segmentation network. Finally, examples are provided on how this method can be extended to find novel segments in images.