Machine Learning Methods for Enhanced Reliable Perception of Autonomous Systems

In our modern life, automated systems are already omnipresent. The latest advances in machine learning (ML) help with increasing automation and the fast-paced progression towards autonomous systems. However, as such methods are not inherently trustworthy and are being introduced into safety-critical systems, additional means are needed. In autonomous driving, for example, we can derive the main challenges when introducing ML in the form of deep neural networks (DNNs) for vehicle perception. DNNs are overconfident in their predictions and assume high confidence scores in the wrong situations. To counteract this, we have introduced several techniques to estimate the uncertainty of the results of DNNs. In addition, we present what are known as out-of-distribution detection methods that identify unknown concepts that have not been learned beforehand, thus helping to avoid making wrong decisions. For the task of reliably detecting objects in 2D and 3D, we will outline further methods. To apply ML in the perception pipeline of autonomous systems, we propose using the supplementary information from these methods for more reliable decision-making. Our evaluations with respect to safety-related metrics show the potential of this approach. Moreover, we have applied these enhanced ML methods and newly developed ones to the autonomous driving use case. In variable environmental conditions, such as road scenarios, light, or weather, we have been able to enhance the reliability of perception in automated driving systems. Our ongoing and future research is on further evaluating and improving the trustworthiness of ML methods to use them safely and to a high level of performance in various types of autonomous systems, ranging from vehicles to autonomous mobile robots, to medical devices.