Aniket Salvi

Autonomous systems are increasingly being used in various safety-critical applications, such as autonomous driving. Especially here, the environment and operating conditions are of crucial importance. Machine Learning components (e.g., Deep Neural Network (DNN) serve as enablers and therefore themselves must continue to operate reliably despite changing environmental conditions. Fraunhofer IKS provides a solution that handles such diverse and continuously changing environmental conditions.

In our previous article, we introduced some standardized terminology related to Autonomous Driving and Operational Design Domain (ODD). In this article, we look at the safety implications of transitioning through multiple ODDs while executing the Dynamic Driving Task (DDT).

The Operational Design Domain (ODD) precisely defines the area in which automated driving functions may be used. As such, they represent a fundamental prerequisite for safe automated driving. Part 1 of a three-part series on the importance of ODD.

Artificial intelligence (AI) has to be able to handle uncertainty before we can trust it to deliver in safety-critical use cases, for example, autonomous cars. The Fraunhofer Institute for Cognitive Systems IKS is investigating ways to help AI reason with uncertainty, one being the operational design domain.