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. One challenge is that, in high dimensional input spaces it is almost impossible to experience enough training data in the design phase. What is required here, is an efficient way to flag out-of-distribution (OOD) samples that is precise enough to not raise too many false alarms. In addition, the system needs to be able to detect these in a computationally efficient manner at runtime. In this paper, our proposal is to build good representations for in-distribution data. We introduce the idea of a memory bank to store prototypical samples from the input space. We use these memories to compute probability density estimates using kernel density estimation techniques. We evaluate our technique on two challenging scenarios : a self-driving car setting implemented inside the simulator CARLA with image inputs, and an autonomous racing car navigation setting, with LiDAR inputs. In both settings, it was observed that a deviation from indistribution setting can potentially lead to deviation from safe behavior. An added benefit of using training samples as memories to detect out-of-distribution inputs is that the system is interpretable to a human operator. Explanation of this nature is generally hard to obtain from pure deep learning based alter-natives. Our code for reproducing the experiments is available at https://github.com/yangy96/interpretable_ood_detection.git