Assured resilience in autonomous systems – automotive and Industry 4.0

Machine learning in safety-critical systems

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As progressive automation brings us closer to autonomous systems, machine learning methods for mapping and processing complex and unknown situations have become indispensable. Autonomous systems with an advanced degree of automation – i.e., highly automatic to fully autonomous systems – often employ neural networks for context recognition. Early results in the field of deep learning appear highly promising for this task, enabling driverless vehicles to recognize objects, interpret traffic events and issue driving instructions. These techniques, which are based on machine learning, can either be used system-wide, that is from the sensor to the actuator, or merely to solve individual aspects of autonomous driving.

New methods for assured resilience in AI techniques

Given that techniques such as neural networks do not lend themselves to external scrutiny and validation, they are however not suitable for use in safety-critical systems like driverless transport systems or autonomous vehicles, thereby hindering the successful introduction of safe autonomous systems. Consequently, the ADA Center is researching new methods to sufficiently assure the resilience of AI procedures in high-performance embedded system platforms. To this end, we are developing monitoring mechanisms that demonstrably monitor all properties crucial to the safety of the autonomous system. In addition, we are developing a flexible resilience concept designed to be usable both for different learning methods, and for different sensor configurations. For instance, if partially explainable machine learning methods (such as Explainable AI) are deployed, this additional information will contribute to resilience assurance. Furthermore, the ADA Center is researching appropriate AI methods for the monitoring itself, for use as redundant paths, or for monitoring of non-explainable machine learning processes.