8 citations · 12 across the 6 of their papers we have counts for
4 papers · 1 filter
A generic physics-informed neural network-based framework for reliability assessment of multi-state systems
Taotao Zhou, Xiaoge Zhang, Enrique Lopez Droguett +1
In this paper, we leverage the recent advances in physics-informed neural network (PINN) and develop a generic PINN-based framework to assess the reliability of multi-state systems…
A Scenario-Based Platform for Testing Autonomous Vehicle Behavior Prediction Models in Simulation
Francis Indaheng, Edward Kim, Kesav Viswanadha +4
Behavior prediction remains one of the most challenging tasks in the autonomous vehicle (AV) software stack. Forecasting the future trajectories of nearby agents plays a critical r…
Addressing the IEEE AV Test Challenge with Scenic and VerifAI
Kesav Viswanadha, Francis Indaheng, Justin Wong +5
This paper summarizes our formal approach to testing autonomous vehicles (AVs) in simulation for the IEEE AV Test Challenge. We demonstrate a systematic testing framework leveragin…
Scenic4RL: Programmatic Modeling and Generation of Reinforcement Learning Environments
Abdus Salam Azad, Edward Kim, Qiancheng Wu +4
The capability of a reinforcement learning (RL) agent heavily depends on the diversity of the learning scenarios generated by the environment. Generation of diverse realistic scena…