Interpretable Detection of Distribution Shifts in Learning Enabled Cyber-Physical Systems

The use of learning based components in cyber-physical systems (CPS) has created a gamut of possible avenues to use high dimensional real world signals generated from sensors like camera and LiDAR. The ability to process such signals can be largely attributed to the adoption of high-capacity function approximators like deep neural networks. However, this does not come without its potential perils. The pitfalls arise from possible over-fitting, and subsequent unsafe behavior when exposed to unknown environments.