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Visible to the public Maneuver and Data Optimization for High Confidence Testing of Future Automotive Cyber-Physical Systems

Our research addresses urgent challenges in high confidence testing of automotive systems due to on-going and anticipated introduction of advanced, connected, and autonomous vehicle technologies. We have pursued the development of tools for maneuver and data optimization to determine test trajectories and scenarios to facilitate vehicle testing. Our approaches exploit game theoretic traffic interaction modeling to inform in-traffic relevant trajectories, model-free optimization to identify trajectories falsifying time domain specifications, and the development of Smart Bla

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Visible to the public Co-state initialization for the minimum-time low-thrust trajectory optimization

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Visible to the public Shaping velocity coordinates for generating low-thrust trajectories

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Visible to the public Visualization-aware sampling for very large databases

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Visible to the public Database learning: Toward a database that becomes smarter every time

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Visible to the public A Reference Governor for Nonlinear Systems Based on Quadratic Programming