Monitoring of safety security-relevant incidents
Developing autonomous acting vehicles requires a working and reliable collaboration between functions, (sub-)systems, and services. These collaborations are not limited to the vehicle itself but continue well into the development cycle. A continuously changing landscape of cyber security risks and the fulfillment of functionality (SOTIF) require dynamic monitoring of a wide range of vehicle sensor data. Deciding which data should be monitored and saved, how this can be achieved and the integration into the next development cycle is an open question that gets many different answers frequently.
Making stochastic components certifiable
Machine learning components are becoming increasingly widespread in a wide range of contexts and achieve remarkable performance. Because of their stochastic nature, their use isn’t deterministic and they often act like a “black box”, which makes certification difficult. Creating frameworks and models to simulate or predict their behavior could yield a more controllable and formal approach of combining the high performance of ML components with the predictability of proven conventional components. These models shouldn’t be limited to ML components but are usable in different scenarios.
Graceful Degradation strategies
The failing of a non-critical subsystem shouldn’t result in a complete failure of a whole mission. Often redundant hardware components are used to reduce the mean time between failure. This at least doubles the installed hardware and makes the whole system more expensive. If the redundant hardware isn’t needed it is idle and takes up unnecessary space and power. To mitigate this, non-critical tasks could be executed on these systems, that might be canceled on demand. Creating sophisticated models and frameworks that predict the behavior of these systems is necessary for predictability and legal certification.
|Safety and Security for machine learning - Layer activation tracing analysis||Bachelorarbeit|
|Monitoring strategies and metrics for AI accelerator activations using activation patterns||Masterarbeit|
|Intrusion detection by describing bus systems as stochastic processes||Masterarbeit|
|Parallel result validation of AI accelerators using most neuron activation monitor||Masterarbeit|