This research aims at finding ways to ensure the safety and validity of autonomous systems; a non-trivial problem. This problem is non-trivial and unique because of two fundamental issues. First, these systems have an unconstrained input space due to them operating in the real world. Second, these systems rely on technology, such as machine learning, that is inherently statistical and tends to be non-deterministic. This research recognizes the dual nature of autonomous systems, namely that they contain both physical and software elements that interact in the real world. The insight that we can harness information from the physical models of autonomous systems as well as information from software analysis is unique and has numerous benefits. For example:
- Harnessing an autonomous system’s physical model to automatically reduce the input size of the problem to only those that are feasible given the systems’ physical constraints. We describe how we did this in more detail in this talk
- Improving the accuracy of a systems physical model by incorporating software constraints into the physical model. We describe this in more detail in this talk.