VeHICaL: Verified Human Interfaces, Control, and Learning for Semi-Autonomous Systems

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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.

Current participants:


Additional participants:

Collaborators:

  • UC Berkeley: Sanjit Seshia (PI), Ruzena Bajcsy, Thomas Griffiths, Claire Tomlin, Shankar Sastry
  • UNC: Cynthia Sturton

Past participants:

References

None to date


  • Agency: NSF
  • Grant number: CNS-1545126
  • Start date: 1 Sep 2016
  • End date: 31 Aug 2021
  • Support: 1 graduate student + materials
  • Reporting: Annual program review + report