Project Grant 2440861

Award Date 6/15/25
Completion Date 5/31/30
Dollars Obligated $652K
Federal Grant Program
47.041
Assistance Type
Project Grant
Place of Performance
Seattle, WA 98195, USA
Similar Awards
This five-year National Science Foundation Project Grant of $1,238,246 will fund research at Carnegie Mellon University toward developing safe autonomous systems. The goal is to enable robots and other autonomous systems to operate safely while interacting closely with humans, as required for applications like next-generation manufacturing infrastructure. The university will develop a new algorithmic framework for assuring safety in autonomous robotic systems. The framework aims to optimize...
This National Science Foundation (NSF) Faculty Early Career Development (CAREER) Program award provides $581,320 in funding to the University of Vermont (UVM) over a 5-year period from June 1, 2024 to May 31, 2029. The research project, titled "A Universal Framework for Safety-Aware Data-Driven Control and Estimation", aims to develop a framework for the simultaneous design of control policies and safety measures for complex systems like robotics and power systems using data-driven...
This National Science Foundation (NSF) CAREER Award under the Engineering (CFDA 47.041) program provides $556,507 in funding to Embry-Riddle Aeronautical University from July 1, 2023 to June 30, 2028. The award supports fundamental research to investigate human driving behavior and interactions among traffic participants, with the goal of enabling technological advances in autonomous and connected vehicles. The research will leverage an immersive virtual reality driving simulator to conduct...
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 $500,000 project grant, awarded on January 1, 2024 by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070), aims to address the urgent need for end-to-end safety in learning-enabled autonomous systems across various application scenarios, such as self-driving cars and urban air mobility. The project, titled "COLLABORATIVE RESEARCH: SLES: GUARANTEED TUBES FOR SAFE LEARNING ACROSS AUTONOMY ARCHITECTURES,"...
This $425,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research to develop a framework for ensuring the safety of future robotic systems. The research project, led by The Trustees of Princeton University, aims to lay the foundation for safe robot autonomy by enabling robots to continually prove the safety of their actions under a wide range of operating conditions, from complex physical...
This $235,187 federal Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) supports research to develop principled algorithms and practical tools for systematically discovering and repairing unsafe behavior in multi-module autonomous vehicle systems. The key objectives are to: (1) create an automated method for constructing test scenarios that decouple high-level semantics and low-level details; (2) develop a search-based testing approach to efficiently...
This $800,000 Project Grant award to The Trustees of Princeton University, Department of Research and Project Administration, was provided by the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070). The award aims to develop new techniques for constructing a vision-based safety supervisor that can endow autonomous robotic systems, such as self-driving cars and home robots, with the safety property of "graceful degradation."...
The Trustees of the University of Pennsylvania received a $500,000 Project Grant award from the National Science Foundation Division of Electrical, Communications and Cyber Systems on February 15, 2021 to support research titled "CAREER: TOWARDS A THEORY OF ROBUST LEARNING & CONTROL FOR SAFETY-CRITICAL AUTONOMOUS SYSTEMS" through January 31, 2026. This award will fund research under the NSF Engineering program (CFDA #47.041) to develop a theoretical framework for robust learning...
This federal Project Grant award of $800,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) is supporting research to develop methods for certifying the safety of autonomous systems that use deep learning-enabled perception, prediction, and control components. The key goals of the project are: (1) to develop techniques for learning safety certificates and control policies for these types of learning-enabled autonomous...

CAREER: CHARACTERIZING THE SAFETY LANDSCAPE: DATA-DRIVEN SAFETY MODELING FOR AUTONOMOUS SYSTEM NAVIGATION AND CONTROL IN HUMAN ENVIRONMENTS -THIS FACULTY EARLY CAREER DEVELOPMENT PROGRAM (CAREER) GRANT FUNDS RESEARCH, EDUCATION, AND OUTREACH INITIATIVES THAT WILL ENABLE AUTONOMOUS SYSTEMS, SUCH AS ROBOTS AND SELF-DRIVING VEHICLES, TO NAVIGATE SAFELY AROUND HUMANS. AS THESE TECHNOLOGIES BECOME INTEGRAL TO TRANSPORTATION, WAREHOUSE LOGISTICS, AND HEALTHCARE, ENSURING HUMAN SAFETY IS PARAMOUNT. THE COMPLEXITY OF REAL-WORLD ENVIRONMENTS, HOWEVER, ARE SHAPED BY COMPLEX AND UNCERTAIN FACTORS SUCH AS SOCIAL BEHAVIORS AND CONTEXTUAL CUES, WHICH PRESENTS SIGNIFICANT CHALLENGES IN ASSESSING AND GUARANTEEING SAFETY. THE RESEARCH ACTIVITIES FUNDED BY THIS AWARD WILL INTEND TO DEVELOP INTERPRETABLE, DATA-DRIVEN SAFETY MODELS THAT ARE CRUCIAL FOR EXPLAINING SAFETY INCIDENTS AND RELATED HUMAN PERCEPTIONS IN ENVIRONMENTS WHERE AUTONOMOUS SYSTEMS OPERATE. THESE MODELS ARE INTENDED TO ENHANCE PREDICTABILITY, IMPROVE OPERATIONAL EFFICIENCY, AND MINIMIZE SAFETY RISKS, THEREBY OPTIMIZING HUMAN-SYSTEM INTERACTIONS. EDUCATIONAL AND OUTREACH EFFORTS FUNDED BY THIS AWARD INCLUDE COLLABORATION WITH HARBORVIEW MEDICAL CENTER TO DEPLOY SAFETY ALGORITHMS IN HOSPITAL ENVIRONMENTS, IMPROVING PATIENT CARE. ADDITIONAL, EDUCATIONAL CURRICULA WILL BE ENRICHED WITH PRACTICAL SAFETY EXPERIENCES TO ENHANCE PRE-ENGINEERING MATH COURSES AT SHORELINE COMMUNITY COLLEGE. THESE INITIATIVES WILL PREPARE FUTURE INNOVATORS AND REGULATORS TO DEVELOP AND MANAGE TRUSTED AUTONOMOUS SYSTEMS. DETERMINING THE SAFETY OF GIVEN SCENARIOS, OR THE ?SAFETY LANDSCAPE,? INVOLVES CHALLENGES FROM UNCERTAINTY, SOCIAL NORMS, CONTEXTUAL CUES, AND FEEDBACK INTERACTIONS. THE SAFETY-CRITICAL NATURE OF HUMAN-ROBOT INTERACTIONS REQUIRES DATA-DRIVEN MODELS TO BE INTERPRETABLE FOR REGULATORY COMPLIANCE, EXPLAINABILITY, AND PREDICTABILITY. THIS RESEARCH DEVELOPS A DATA-DRIVEN FRAMEWORK WITH CONTROL-THEORETIC FOUNDATIONS TO ADDRESS THESE COMPLEXITIES AND PROVIDE CLEAR OUTPUTS FOR DECISION-MAKING AND CONTROL. THE RESEARCH ENCOMPASSES THREE THRUSTS: (I) DATA GENERATION, DESIGNING SYSTEMS FOR COLLECTING SAFETY-CRITICAL DATA; (II) ANALYSIS AND MODELING, USING CONTROL-THEORETIC TECHNIQUES TO DECODE COMPLEX INTERACTIONS; AND (III) CONTROL SYNTHESIS, CREATING SOCIALLY-AWARE STRATEGIES FOR SAFER INTERACTIONS. THE DATA-DRIVEN FRAMEWORK WILL BE EVALUATED USING PUBLIC HUMAN NAVIGATION DATASETS, SIMULATED HUMAN-IN-THE-LOOP EXPERIMENTS, AND REAL-WORLD TESTING AT A HOSPITAL RESEARCH FACILITY. THIS AWARD REFLECTS NSF'S STATUTORY MISSION AND HAS BEEN DEEMED WORTHY OF SUPPORT THROUGH EVALUATION USING THE FOUNDATION'S INTELLECTUAL MERIT AND BROADER IMPACTS REVIEW CRITERIA.- SUBAWARDS ARE NOT PLANNED FOR THIS AWARD.

Posted 6/5/25, 12:00 AM