Short Description
Cyber-Physical Systems (CPS) are commonly used in mission-critical or safety-critical applications which demand high reliability and strong assurance for safety. These systems frequently operate in highly uncertain environments where it is infeasible to explicitly design for all possible situations within the environment. Assuring safety in these systems requires supporting evidence from testing data, formal verification, expert analysis, etc. Data-driven methods, such as machine learning, are being applied in CPS development to address these challenges.
The Assurance-based Learning-enabled Cyber-Physical Systems (ALC) toolchain is an integrated set of tools and corresponding workflows specifically tailored for model-based development of CPSs that utilize learning-enabled components (or LECs). Machine learning relies on inferring relationships from data instead of deriving them from analytical models, leading many systems employing LECs to rely almost entirely on testing results as the primary source of evidence. However, test data alone is generally insufficient for assurance of safety-critical systems to detect all the possible edge cases. This set of tools support various tasks including architectural system modeling, data construction of experimental data and LEC training sets, performance evaluation using formal verification methods and system safety assurance monitoring. Fig. 1 shows the general order activity for each of these steps. Each step of the process can be refined through iterations to adjust parameters, retrain LECs, adjust testing solution spaces, etc.
Evidence used for safety assurance should be traceable and reproducible. Since LECs are trained with data instead of derived from analytical models, the quality of an LEC is dependent on the history and quality of the training data. Therefore, it is necessary to maintain data provenance when working with LECs to allow the model to be reproducible. Manual data management across the complex tool suites often used for CPS development is a time consuming and error-prone process. This issue is even more pronounced for systems using LECs where training data and the resulting trained models must also be properly managed. With this toolchain, all generated artifacts - including system models, simulation data, trained networks, etc. - are automatically stored as accessible data sets and managed to allow for both traceability and reproducibility.
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Design Studio
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Links
Contact: alc-team@vanderbilt.edu
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Selected Publications
Model-Based Design for CPS with Learning-Enabled Components
CPS Design with Learning-Enabled Components: A Case Study
Workflow Automation for Cyber Physical System Development Processes