  In this paper, we propose a method for addressing the issue of unnoticed
catastrophic deployment and domain shift in neural networks for semantic
segmentation in autonomous driving. Our approach is based on the idea that deep
learning-based perception for autonomous driving is uncertain and best
represented as a probability distribution. As autonomous vehicles' safety is
paramount, it is crucial for perception systems to recognize when the vehicle
is leaving its operational design domain, anticipate hazardous uncertainty, and
reduce the performance of the perception system. To address this, we propose to
encapsulate the neural network under deployment within an uncertainty
estimation envelope that is based on the epistemic uncertainty estimation
through the Monte Carlo Dropout approach. This approach does not require
modification of the deployed neural network and guarantees expected model
performance. Our defensive perception envelope has the capability to estimate a
neural network's performance, enabling monitoring and notification of entering
domains of reduced neural network performance under deployment. Furthermore,
our envelope is extended by novel methods to improve the application in
deployment settings, including reducing compute expenses and confining
estimation noise. Finally, we demonstrate the applicability of our method for
multiple different potential deployment shifts relevant to autonomous driving,
such as transitions into the night, rainy, or snowy domain. Overall, our
approach shows great potential for application in deployment settings and
enables operational design domain recognition via uncertainty, which allows for
defensive perception, safe state triggers, warning notifications, and feedback
for testing or development and adaptation of the perception stack.
