Risk-Aware Scene Sampling for Dynamic Assurance of Autonomous Systems

Autonomous Cyber-Physical Systems must often operate under uncertainties like sensor degradation and shifts in the operating conditions, which increases its operational risk. Dynamic Assurance of these systems requires designing runtime safety components like Out-of-Distribution detectors and risk estimators, which require labeled data from different operating modes of the system that belong to scenes with adverse operating conditions, sensors, and actuator faults. Collecting real-world data of these scenes can be expensive and sometimes not feasible. So, scenario description languages with samplers like random and grid search are available to generate synthetic data from simulators, replicating these real-world scenes. However, we point out three limitations in using these conventional samplers. First, they are passive samplers, which do not use the feedback of previous results in the sampling process. Second, the variables to be sampled may have constraints that are often not included. Third, they do not balance the tradeoff between exploration and exploitation, which we hypothesize is necessary for better search space coverage. We present a scene generation approach with two samplers called Random Neighborhood Search (RNS) and Guided Bayesian Optimization (GBO), which extend the conventional random search and Bayesian Optimization search to include the limitations. Also, to facilitate the samplers, we use a risk-based metric that evaluates how risky the scene was for the system. We demonstrate our approach using an Autonomous Vehicle example in CARLA simulation. To evaluate our samplers, we compared them against the baselines of random search, grid search, and Halton sequence search. Our samplers of RNS and GBO sampled a higher percentage of high-risk scenes of 83% and 92%, compared to 56%, 66% and 71% of the grid, random and Halton samplers, respectively.

Stratum: A Serverless Framework for Lifecycle Management of Machine Learning based Data Analytics Tasks

With the proliferation of machine learning (ML) libraries and frameworks, and the programming languages that they use, along with operations of data loading, transformation, preparation and mining, ML model development is becoming a daunting task. Furthermore, with a plethora of cloud-based ML model development platforms, heterogeneity in hardware, increased focus on exploiting edge computing resources for low-latency prediction serving and often a lack of a complete understanding of resources required to execute ML workflows efficiently, ML model deployment demands expertise for managing the lifecycle of ML workflows efficiently and with minimal cost. To address these challenges, we propose an end-to-end data analytics, a serverless platform called Stratum. Stratum can deploy, schedule and dynamically manage data ingestion tools, live streaming apps, batch analytics tools, ML-as-a-service (for inference jobs), and visualization tools across the cloud-fog-edge spectrum. This paper describes the Stratum architecture highlighting the problems it resolves.