5 papers
SeFaR: Semantic Feature-aware Robustness Testing of Deep Neural Networks
Nusrat Jahan Mozumder, Divya Gopinath, Corina Pasareanu +1
Deep neural networks are increasingly deployed in safety-critical domains as perception modules, where failures are often caused due to rare and under-represented scenarios. This n…
STADA: Specification-based Testing for Autonomous Driving Agents
Joy Saha, Trey Woodlief, Sebastian Elbaum +1
Simulation-based testing has become a standard approach to validating autonomous driving agents prior to real-world deployment. A high-quality validation campaign will exercise an…
LabelAny3D: Label Any Object 3D in the Wild
Jin Yao, Radowan Mahmud Redoy, Sebastian Elbaum +2
Detecting objects in 3D space from monocular input is crucial for applications ranging from robotics to scene understanding. Despite advanced performance in the indoor and autonomo…
RBT4DNN: Requirements-based Testing of Neural Networks
Nusrat Jahan Mozumder, Felipe Toledo, Swaroopa Dola +1
Testing allows developers to determine whether a system functions as expected. When such systems include deep neural networks (DNNs), Testing becomes challenging, as DNNs approxima…
Quantitative Predictive Monitoring and Control for Safe Human-Machine Interaction
Shuyang Dong, Meiyi Ma, Josephine Lamp +3
There is a growing trend toward AI systems interacting with humans to revolutionize a range of application domains such as healthcare and transportation. However, unsafe human-mach…