VeHICaL: Verified Human Interfaces, Control, and Learning for Semi-Autonomous Systems
The VEHICaL project is developing the foundations of verified co-design of interfaces and control for human cyber-physical systems (h-CPS) --- cyber-physical systems that operate in concert with human operators. VeHICaL aims to bring a formal approach to designing both interfaces and control for h-CPS, with provable guarantees. The project is making contributions along four thrusts: (1) formalisms for modeling h-CPS; (2) computational techniques for learning, verification, and control of h-CPS; (3) design and validation of sensor and human-machine interfaces, and (4) empirical evaluation in the domain of semi-autonomous vehicles. The VeHICaL approach is bringing a conceptual shift of focus away from separately addressing the design of control systems and human-machine interaction and towards the joint co-design of human interfaces and control using common modeling formalisms and requirements on the entire system. This co-design approach is making novel intellectual contributions to the areas of formal methods, control theory, sensing and perception, cognitive science, and human-machine interfaces.
Caltech will participate in research related to specification, design and verification of networked control systems with applications to human-controlled cyberphysical sys- tems (h-CPS). Working jointly with researchers at UC Berkeley, Caltech will extend previous work in synthesis of control protocols for hybrid systems to include interactions with humans and the applications to semi-autonomous vehicles. Caltech will support all program reviews and annual technical reports, in addition to participating in outreach activities.
- Contracts of Reactivity (Tung Phan-Minh and Richard M. Murray, Submitted, Int'l Conf on Formal Modeling and Analysis of Timed Systems (FORMATS) 2019)
- Towards Assume-Guarantee Profiles for Autonomous Vehicles (Tung Phan-Minh, Karena X. Cai, Richard M. Murray, Submitted, 2019 Conference on Decision and Control (CDC))
- Inverse Abstraction of Neural Networks Using Symbolic Interpolation (Sumanth Dathathri, Sicun Gao, Richard M. Murray, To appear, 2019 AAAI Conference on Artificial Intelligence)
- Risk-aware motion planning for automated vehicle among human-driven cars (Jin I. Ge, Bastian Schurmann, Richard M. Murray, and Matthias Althoff, Submitted, 2019 American Control Conference (ACC))
- Voluntary lane-change policy synthesis with reactive control improvisation (Jin I. Ge and Richard M. Murray, To appear, 2018 Conference on Decision and Control (CDC))
Research supported by the National Science Foundation award CNS-1545126.