5 citations · 7 across the 11 of their papers we have counts for
13 papers
Forward-Invariant Policy Classes for Safe Reinforcement Learning in Multicopter Control
Chieh Tsai, Muhammad Junayed Hasan Zahed, Jinzhi Shen +5
This paper proposes a reinforcement learning (RL) framework for safe gain scheduling based on forwardinvariance-induced action-space design. Under stated nominalmodel and inversion…
Federated Cybersecurity Testbed as a Service (FCTaaS): A framework to federate cybersecurity testbeds
Josh Dean, Yu-Zheng Lin, John Paul Martin Encinas +9
Rapid technological change is reshaping society through emerging domains such as autonomous vehicles and smart manufacturing, creating new research challenges in system design, ope…
Mission-Level Runtime Assurance Framework for Autonomous Driving
Chieh Tsai, Salim Hariri
This paper studies runtime safety for autonomous driving when high-level driving commands become faulty or unreliable. Unlike conventional runtime-safety approaches that mainly foc…
Online Reinforcement Learning for Safe Gain Scheduling in Nonlinear Quadrotor Control
Muhammad Junayed Hasan Zahed, Chieh Tsai, Salim Hariri +1
This paper presents an online reinforcement-learning framework for safe gain scheduling of a nonlinear quadcopter controller. Rather than learning thrust and torque commands direct…
RACF: A Resilient Autonomous Car Framework with Object Distance Correction
Chieh Tsai, Hossein Rastgoftar, Salim Hariri
Autonomous vehicles are increasingly deployed in safety-critical applications, where sensing failures or cyberphysical attacks can lead to unsafe operations resulting in human loss…
Security and Resilience in Autonomous Vehicles: A Proactive Design Approach
Chieh Tsai, Murad Mehrab Abrar, Salim Hariri
Autonomous vehicles (AVs) promise efficient, clean and cost-effective transportation systems, but their reliance on sensors, wireless communications, and decision-making systems ma…
Learning over Forward-Invariant Policy Classes: Reinforcement Learning without Safety Concerns
Chieh Tsai, Muhammad Junayed Hasan Zahed, Salim Hariri +1
This paper proposes a safe reinforcement learning (RL) framework based on forward-invariance-induced action-space design. The control problem is cast as a Markov decision process,…
Autonomous Vehicle Security: A Deep Dive into Threat Modeling
Amal Yousseef, Shalaka Satam, Banafsheh Saber Latibari +4
Autonomous vehicles (AVs) are poised to revolutionize modern transportation, offering enhanced safety, efficiency, and convenience. However, the increasing complexity and connectiv…
Aerial-based Crisis Management Center (ACMC)
Hossein Rastgoftar, Salim Hariri
Crisis management (CM) for critical infrastructures, natural disasters such as wildfires and hurricanes, terrorist actions, or civil unrest requires high speed communications and c…
Development of an Edge Resilient ML Ensemble to Tolerate ICS Adversarial Attacks
Likai Yao, Qinxuan Shi, Zhanglong Yang +2
Deploying machine learning (ML) in dynamic data-driven applications systems (DDDAS) can improve the security of industrial control systems (ICS). However, ML-based DDDAS are vulner…
GPS-IDS: An Anomaly-based GPS Spoofing Attack Detection Framework for Autonomous Vehicles
Murad Mehrab Abrar, Amal Youssef, Raian Islam +6
Autonomous Vehicles (AVs) heavily rely on sensors and communication networks like Global Positioning System (GPS) to navigate autonomously. Prior research has indicated that networ…
Quantized Transformer Language Model Implementations on Edge Devices
Mohammad Wali Ur Rahman, Murad Mehrab Abrar, Hunter Gibbons Copening +4
Large-scale transformer-based models like the Bidirectional Encoder Representations from Transformers (BERT) are widely used for Natural Language Processing (NLP) applications, whe…
An Anomaly Behavior Analysis Framework for Securing Autonomous Vehicle Perception
Murad Mehrab Abrar, Salim Hariri
As a rapidly growing cyber-physical platform, Autonomous Vehicles (AVs) are encountering more security challenges as their capabilities continue to expand. In recent years, adversa…