Develop efficient algorithms for autonomous systems to characterize and actively identify the inconsistencies (e.g., in perception and decision-making) among the interacting agents and to influence the behaviors of the other agents to improve the safety of the overall system.
Develop specification formalisms, control synthesis algorithms, and quantitative verification frameworks for autonomous systems that include learning-based components, operate in uncertain environments, and are subject to conflicting requirements with partially established priorities.
Develop runtime verification techniques that incorporate mixed-abstraction-level granularity in specifications and enable on-deadline mitigation triggering
Develop planning and decision-making algorithms with multiple, potentially conflicting objectives.
Derive, analyze, and refine specifications from regulatory requirements and demonstrations.
A Python-based software toolbox for the synthesis of embedded control software that is provably correct with respect to an expressive subset of linear temporal logic (LTL) specifications.
Develop mathematical and computational frameworks to facilitate the design and analysis of embedded control systems such as autonomous vehicles.
A race of autonomous ground vehicles through an urban environment.