Project Grant 2541931
- This five-year, $673,782 National Science Foundation project grant in the Engineering program (CFDA 47.041) will fund research at Purdue University to develop a control-theoretic framework for modeling human cognitive state dynamics during human-automation interaction. The framework will define a human cognitive state space and characterize its dynamics in a model that can be used for control system design. A methodology will be created to estimate cognitive states and parameters in real time...
- This $316,963 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program will support research to enhance the safety and reliability of autonomous vehicles. The project aims to thoroughly examine and improve the controller and machine learning components of autonomous driving systems through a combination of model-based and data-driven approaches. The research will focus on identifying spatial and temporal vulnerabilities that...
- This $272,238 Project Grant, awarded on July 15, 2024 by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), supports research to enhance the safety and reliability of autonomous vehicles. The project aims to identify vulnerabilities in the software and machine learning components of autonomous vehicle systems, and develop mitigation techniques to improve their overall resilience. The research will combine model-based and...
- This $1,499,949 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will fund research to enhance the safety of autonomous vehicle (AV) systems. The key focus areas include: Developing rational machine learning (ML) models that can accurately predict driving decisions based on valid rationales, rather than inappropriate extrapolations from common scenarios. Integrating hardware reliability into the...
- This $298,747 Project Grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program will support collaborative research between the University of California, Irvine (UCI) and the Indian Institutes of Technology (IIT) in Kharagpur and Jodhpur. The project aims to investigate the security and robustness of Collaborative Autonomous Driving (COAD) systems, which consist of connected and autonomous vehicles (CAVs) and...
- This $300,726 federal Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CFDA 47.070) program supports research to investigate the security and resiliency of Collaborative Autonomous Driving (COAD) systems. The project, led by researchers at the University at Buffalo and UC Irvine in the U.S. and the Indian Institutes of Technology at Kharagpur and Jodhpur in India, aims to develop a comprehensive framework for examining new attack...
- This $120,000 Project Grant was awarded on July 1, 2024 by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to the Regents of the University of California at Riverside (UC Riverside). The funding will support a collaborative research project focused on enhancing the safety and reliability of autonomous driving systems through the development of advanced detection and countermeasure techniques at the application, system, and...
- This $482,972 National Science Foundation (NSF) Engineering program (CFDA 47.041) grant supports fundamental research at North Carolina State University (NC State) to understand the behavior and interactions of mixed autonomous and human-driven vehicle traffic streams. The project aims to: (1) characterize the driving behaviors of human-driven and connected automated vehicles in conflicting traffic, (2) analyze the collective impact of mixed traffic on traffic flow, and (3) develop robust...
- This National Science Foundation (NSF) Project Grant award under the Computer and Information Science and Engineering (CFDA 47.070) program provides $338,536 to George Mason University to enhance the safety and reliability of autonomous vehicle systems. The project aims to identify and mitigate vulnerabilities in the control software and machine learning components of autonomous vehicles through a combination of model-based and data-driven approaches for end-to-end resilience assessment and...
- This federal Project Grant award of $273,555.00, provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070), supports a collaborative research initiative led by the University of Delaware. The goal of the project, titled "CADAAS: Deterministic Communication and Predictive Computation for Connected Autonomous Driving as a Service," is to advance autonomous driving capabilities by integrating information...
Federal Project Grant Award Summary The National Science Foundation (NSF), Division of Computing and Communication Foundations, awarded Purdue University a $357,107 Project Grant (CFDA 47.070: Computer and Information Science and Engineering) effective October 1, 2026, through September 30, 2031, to develop adaptive shared control systems for automated vehicles that predict human driver cognitive and intent states. The research will deliver methods and inference models enabling vehicles to interpret occupant visual attention, spoken language, and physical actions in real-time, then adjust vehicle behavior accordingly through a shared-control framework. The project will produce behavioral sensing technologies, cognitive state estimation models, personalized interaction algorithms that account for individual driver differences, and a decision-making system that determines when the vehicle should act independently, defer to the occupant, or intervene. Results will be validated through simulation and testing on an instrumented vehicle platform. The grant supports fundamental research addressing current automated vehicle safety gaps by moving beyond one-size-fits-all system design to partner-based interaction models. By developing technologies that align vehicle behavior with human expectations, the research aims to reduce unsafe interactions, improve occupant trust in automation, and facilitate broader adoption of automated transportation systems. The framework and methodologies developed are expected to have broader applicability to other safety-critical domains requiring human-machine collaboration and coordination.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $357.1k | 7/11/26 |