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Brain digital twins aim to provide faithful, individualized computational representations of brains as dynamical systems, enabling mechanistic understanding and supporting prediction of clinical interventions. Yet current approaches remain…
Embedded software systems, e.g. automotive, robotic or automation systems are highly configurable and consist of many software components being available in different variants and versions. To identify the degree of reusability between…
A Digital Twin (DT) is a digital representation of a physical object used to simulate it before it is built or to predict failures after the object is deployed. In this article, we introduce our approach, which applies the concept of a…
Simulation offers a scalable and efficient alternative to real-world data collection for learning visuomotor robotic policies. However, the simulation-to-reality, or Sim2Real distribution shift -- introduced by employing simulation-trained…
The concept of a digital twin (DT) plays a pivotal role in the ongoing digital transformation and has achieved significant strides for various wireless applications in recent years. In particular, the field of autonomous vehicles is a…
Machine learning provides a data-driven approach for creating a digital twin of a system - a digital model used to predict the system behavior. Having an accurate digital twin can drive many applications, such as controlling autonomous…
As a promising technology, vehicular edge computing (VEC) can provide computing and caching services by deploying VEC servers near vehicles. However, VEC networks still face challenges such as high vehicle mobility. Digital twin (DT), an…
Recent advances in sensing technologies, wireless communications, and computing paradigms drive the evolution of vehicles in becoming an intelligent and electronic consumer products. This paper investigates enabling digital twins in…
This work investigates the use of digital twins for dynamical system modeling and control, integrating physics-based, data-driven, and hybrid approaches with both traditional and AI-driven controllers. Using a miniature greenhouse as a test…
Deep learning models have created great opportunities for data-driven fault diagnosis but they require large amount of labeled failure data for training. In this paper, we propose to use a digital twin to support developing data-driven…
Digital twins have become popular for their ability to monitor and optimize a process or a machine, ideally through its complete life cycle using simulations and sensor data. In this paper, we focus on the challenge of accurate and…
This paper presented a deep reinforcement learning method named Double Deep Q-networks to design an end-to-end vision-based adaptive cruise control (ACC) system. A simulation environment of a highway scene was set up in Unity, which is a…
With the development of V2X technology, multiple vehicles cooperative control has been widely studied. However, filed testing is rarely conducted due to financial and safety considerations. To solve this problem, this study proposes a…
Mechanistic models encode scientific knowledge about dynamical systems and are widely used in downstream scientific and policy applications. Recent work has explored LLM-based agentic frameworks to automatically construct mechanistic models…
The next generation networks offers significant potential to advance Intelligent Transportation Systems (ITS), particularly through the integration of Digital Twins (DTs). However, ensuring the uninterrupted operation of DTs through…
Scenario-based testing using simulations is a cornerstone of Autonomous Vehicles (AVs) software validation. So far, developers needed to choose between low-fidelity 2D simulators to explore the scenario space efficiently, and high-fidelity…
In today's technology-driven world, early-stage software development and testing are crucial. Virtual Platforms (VPs) have become indispensable tools for this purpose as they serve as a platform to execute and debug the unmodified target…
This paper addresses the challenging problem of enabling reliable immersive teleoperation in scenarios where an Unmanned Aerial Vehicle (UAV) is remotely controlled by an operator via a cellular network. Such scenarios can be quite critical…
Autonomous vehicles need safe development and testing environments. Many traffic scenarios are such that they cannot be tested in the real world. We see hybrid photorealistic simulation as a viable tool for developing AI (artificial…
The evolution and growing automation of collaborative robots introduce more complexity and unpredictability to systems, highlighting the crucial need for robot's adaptability and flexibility to address the increasing complexities of their…