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 $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 $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 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 $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 $270,913 federal Project Grant award, funded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program, supports research to develop qualitative and quantitative methodologies for assessing the safety of learning-enabled autonomous systems. The project, led by the Augusta University Research Institute, Inc. (AURI), will target foundational challenges in capturing uncertainties from environments and providing timely, comprehensive, and...
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 Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to develop techniques to efficiently and effectively detect and remove defects in autonomous vehicle software through simulations. The $163,351 award to the University of California, Irvine will produce methods to automatically generate driving scenarios that reveal errors, accurately consider driving context and traffic laws to identify...
This $799,803 Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports the development of a new framework named CRASH (Challenging Reinforcement-Learning Based Adversarial Scenarios for Safety Hardening) to effectively stress test and improve the safety of autonomous vehicle software. The framework leverages multi-agent adversarial deep reinforcement learning to automatically generate realistic and challenging...
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...