Managing Uncertainty of AI-based Perception for Autonomous Systems
With the advent of autonomous systems, machine perception is a decisive safety-critical part to make such systems become reality. However, presently used AI-based perception does not meet the required reliability for usage in real-world systems beyond prototypes, as for autonomous cars. In this work, we describe the challenge of reliable perception for autonomous systems. Furthermore, we identify methods and approaches to quantify the uncertainty of AI-based perception. Along with dynamic management of the safety, we show a path to how uncertainty information can be utilized for the perception, so that it will meet the high dependability demands of life-critical autonomous systems.