Safety Monitoring of Neural Networks Using Unsupervised Feature Learning and Novelty Estimation
Artikel i vetenskaplig tidskrift, 2022

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 BDD100k. 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.


Image segmentation


Safety Systems

Machine Learning

Anomaly detection


Autonomous Vehicles



Training data


Arian Ranjbar

Chalmers, Elektroteknik, System- och reglerteknik, Mekatronik

Sascha Hornauer

University of California

Jonas Fredriksson

Chalmers, Elektroteknik, System- och reglerteknik, Mekatronik

Stella Yu

University of California

Ching Yao Chan

University of California

IEEE Transactions on Intelligent Vehicles

23798858 (eISSN)

Vol. In Press


Bioinformatik (beräkningsbiologi)


Datorseende och robotik (autonoma system)



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