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Cyber-Physical Systems Virtual Organization
Read-only archive of site from September 29, 2023.
CPS-VO
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Projects
CPS: Frontier: Collaborative Research: VeHICaL: Verified Human Interfaces, Control, and Learning for Semi-Autonomous Systems
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Submitted by saseshia on Thu, 01/11/2018 - 10:03am
Project Details
Lead PI:
Sanjit Seshia
Co-PI(s):
Tom Griffiths
S. Sastry
Claire Tomlin
Ruzena Bajcsy
Performance Period:
09/01/16
-
08/31/21
Institution(s):
University of California-Berkeley
Sponsor(s):
National Science Foundation
Award Number:
1545126
789 Reads. Placed 495 out of 804 NSF CPS Projects based on total reads on all related artifacts.
Abstract:
This NSF Cyber-Physical Systems (CPS) Frontier project "Verified Human Interfaces, Control, and Learning for Semi-Autonomous Systems (VeHICaL)" 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 VeHICaL project is grounded in a novel problem formulation that elucidates the unique requirements on h-CPS including not only traditional correctness properties on autonomous controllers but also quantitative requirements on the logic governing switching or sharing of control between human operator and autonomous controller, the user interface, privacy properties, etc. 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. Cyber-physical systems deployed in societal-scale applications almost always interact with humans. The foundational work being pursued in the VeHICaL project is being validated in two application domains: semi-autonomous ground vehicles that interact with human drivers, and semi-autonomous aerial vehicles (drones) that interact with human operators. A principled approach to h-CPS design --- one that obtains provable guarantees on system behavior with humans in the loop --- can have an enormous positive impact on the emerging national ``smart'' infrastructure. In addition, this project is pursuing a substantial educational and outreach program including: (i) integrating research into undergraduate and graduate coursework, especially capstone projects; (ii) extensive online course content leveraging existing work by the PIs; (iii) a strong undergraduate research program, and (iv) outreach and summer programs for school children with a focus on reaching under-represented groups.
Related Artifacts
Presentations
VeHICaL- Verified Human Interfaces, Control, and Learning for Semi-Autonomous Systems
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VeHICaL: Verified Human Interfaces, Control, and Learning for Semi-Autonomous Systems
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Posters
Rules of the Road: Formal Guarantees for Autonomous Vehicles with Behavioral Contract Design
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Counterexample-Guided Synthesis of Perception Models and Control
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Finding Safety-Critical Causes of Mode Confusion Using Model Checking
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Control Improvisation for Cyber-Physical Systems
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Learning and Teaching Task Specifications from Demonstrations
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An Efficient Reachability-Based Framework for Provably Safe Navigation in Novel Environments
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Towards Assume-Guarantee Profiles for Autonomous Vehicles
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Learning and Teaching Task Specifications from Demonstrations
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Control Improvisation in Vehicle Modeling and Control
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Logical Clustering and Learning for Time-Series Data
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Human Reward Functions: Learning and Robustness for Interaction-Aware Control
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Publications
Goal-Driven Dynamics Learning via Bayesian Optimization
FaSTrack: a Modular Framework for Fast and Guaranteed Safe Motion Planning
Ensuring safety for sampled data systems: An efficient algorithm for filtering potentially unsafe input signals
Logical Clustering and Learning for Time-Series Data
Compositional Falsification of Cyber-Physical Systems with Machine Learning Components
Active Preference-Based Learning of Reward Functions
Information Gathering Actions Over Human Internal State
Towards trustworthy automation: User interfaces that convey internal and external awareness
Optimizing the Information-Performance Tradeoff between Humans and Autonomy via Information Constraints on Design
Videos
VeHICaL: Verified Human Interfaces, Control, and Learning for Semi-Autonomous Systems
Control Improvisation for Cyber-Physical Systems
Other
Modeling and Influencing Human Attentiveness in Autonomy-to-Human Perception Hand-offs
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