Adaptively Managing Reliability of Machine Learning Perception under Changing Operating Conditions

Autonomous systems are deployed in various contexts, which makes the role of the surrounding environment and operational context increasingly vital, e.g., for autonomous driving. To account for these changing operating conditions, an autonomous system must adapt its behavior to maintain safe operation and a high level of autonomy. Machine Learning (ML) components are generally being introduced for perceiving an autonomous system's environment, but their reliability strongly depends on the actual operating conditions, which are hard to predict. Therefore, we propose a novel approach to learn the influence of the prevalent operating conditions and use this knowledge to optimize reliability of the perception through self-adaptation. Our proposed approach is evaluated in a perception case study for autonomous driving. We demonstrate that our approach is able to improve perception under varying operating conditions, in contrast to the state-of-the-art. Besides the advantage of interpretability, our results show the superior reliability of ML-based perception.