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<title>Tutorial | MECC 2022</title>
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<h1 >Tutorials</h1>
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<h1><span style="color: #535ba0">Tutorial: Modeling, Estimation, and Control for Single Molecule Investigation </span></h1>
<p>Tutorial Session TuCT4, Tuesday October 4, 16:00-17:30, America I & II
<p><strong>Organizer:</strong> Murti V. Salapaka, University of Minnesota, Twin Cities
<p><strong>Abstract</strong>
<br>
Studying bio-molecules one at a time provides a unique view of their behavior and function, a perspective unavailable to bulk studies hindered by averaging effects. Single molecule experiments have led to the discovery of the mechanisms of several fatal and currently incurable diseases such as Alzheimer’s, Amyotrophic Lateral Sclerosis, Duchenne Muscular Dystrophy and more. This advancement was enabled by the engineering of several Nobel prize winning molecular probes, such as the Atomic Force Microscope, Optical Tweezers, and Super Resolution Microscopy. However, performing single molecule experiments with high fidelity still has several challenges due to extremely small sizes and stochastic nature of molecules needed to be studied and the noise inherent in the systems leading to unreliable data interpretation. There remain many more avenues for engineering to push the boundaries of single molecule science.
<br><br>
The main objective of this tutorial is to provide a brief overview of existing single molecule techniques. The tutorial will familiarize the audience with instrumentation and associated software tools needed to perform experiments and efficiently analyze experimental data, while showcasing examples where advanced controls and estimation strategies have provided answers to some challenging problems. This tutorial will also demonstrate novel biochemistry required for advanced controls and estimation techniques. The interplay between instrumentation, advanced controls and estimation techniques, and biochemistry will be illustrated via a case study combining all the above-mentioned techniques. This will enable researchers from the modeling, estimation, and controls community to identify new avenues for contributing towards discoveries at a single molecule limit.
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<h1><span style="color: #535ba0">Tutorial: Control and Testing of Connected and Automated Vehicles </span></h1>
<p>Tutorial Session TuCT1, Tuesday October 4, 16:00-17:30, Liberty I
<p><strong>Organizer:</strong> Yunli Shao, Oak Ridge National Laboratory
<p><strong>Abstract</strong>
<br>
Connectivity enables vehicles to communicate with other surrounding vehicles and infrastructure, extending the line-of-sight of drivers or automated driving systems (ADS) to road and traffic conditions. With vehicle automation, connected and automated vehicles (CAVs) can anticipate future driving situations and be controlled in an intelligent and proactive way to further benefit current vehicular and transportation systems. A CAV is a complex system that involves fusion of multiple technologies such as sensing, perception, localization, communication, traffic prediction, motion planning, optimization, and vehicle controls. CAV technologies require collaborative research efforts from various domains. This tutorial session features researchers from national laboratories, universities, and industries presenting state-of-the-art methodologies on traffic prediction, optimization and control, testing and evaluation for connected and automated vehicles. <br><br>
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<h1><span style="color: #535ba0">Session Talks </span></h1>
<p><strong>Control and Testing Connected and Automated Vehicles in Multi-resolution X-in-the-loop Simulation</strong></p>
<p><strong>Yunli Shao</strong> , R&D Staff, Oak Ridge National Laboratory
<p><strong>Abstract</strong>
<br>
CAV technologies need comprehensive testing and evaluation before actual implementation in the real world. However, many inherent technical challenges exist due to the complexity of CAVs. An integrated evaluation platform is needed with vehicle and traffic simulation from different resolutions and X-in-the-loop (XIL) components to fully evaluate all aspects of CAV technologies. In this work, a multi-resolution XIL simulation framework named Real-Sim is presented to support inclusive testing and evaluation of CAVs with co-simulation of various vehicle and traffic simulation tools with different XIL systems. Real-Sim supports perception sensor and communication emulation to test various advanced driver-assistance systems, automated driving systems, and connected vehicle technologies. In addition, a unified scenario definition and streamline scenario generation capability is included so that “standardized” scenarios can be transferrable among different entities to understand CAV impacts in a consistent and scalable fashion. The proposed Real-Sim framework is demonstrated experimentally with several CAV optimization and control algorithms in various vehicle and traffic simulation tools which shows the flexibility of Real-Sim’s approach and potential use cases.
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<br><br>
<p><strong>Real-time Traffic Prediction for Connected and Autonomous Vehicles</strong></p>
<p><strong>Zongxuan Sun</strong>, Professor, University of Minnesota
<p><strong>Abstract</strong>
<br>
Through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, traffic information such as preceding CAVs’ speed and location information would be available. By using the traffic flow model and state observer, other unknown states could also be estimated. By propagating these traffic states, future traffic states would be available to predict a specific preceding vehicle’s trajectory. Such information is crucial for controlling connected and autonomous vehicles CAV). To optimize the speed and powertrain operation of a CAV, an optimization problem can be formulated for a future time horizon of 10-15 seconds. The preceding vehicle’s future trajectory can be used as a constraint for the optimization problem. Both experimental and simulation results will be presented to illustrate the effectiveness of the real-time traffic predication.
</p>
<br><br>
<p><strong>Development and Validation of Eco-Autonomous Driving System</strong></p>
<p><strong>Junfeng Zhao</strong>, Arizona State University
<p><strong>Abstract</strong>
<br>
Connected and automated vehicle (CAV) is a transformative technology that could significantly reduce traffic accidents and improve the efficiency of transportation systems. There is also an increasing interest in the potential of future CAVs for reducing energy consumption in transportation sector. In this talk, I will introduce eco-autonomous driving algorithm and system, which could substantially improve the energy efficiency by leveraging preview information as well as vehicle controls. I will talk about how the eco-autonomous driving algorithm is developed and implemented on top of a canonical software architecture for L3/L4 automated driving. Meanwhile, it has been a very challenging task to conduct quantitative real-world benefit assessment of eco-driving. I will show a comprehensive set of validation approaches, including large-scale simulation, in-depth simulation, vehicle-in-the-loop testing and road testing, which systematically support algorithm development and validation. I will highlight how the demo vehicle was instrumented with the sensing, connectivity, planning and controls modules. This demo vehicle is capable of conducting ecoautonomous driving, and communicating with traffic lights via V2I connectivity in real traffic environment.
</p>
<br><br>
<p><strong>AV/ADAS scenario-based modeling and simulation</strong></p>
<p><strong>Yuming Niu</strong>, Connected Modeling & Simulation Research, Research and Advanced Engineering, Ford Motor Company
<p><strong>Abstract</strong>
<br>
AV/ADAS technologies present enormous and unprecedent challenges to vehicle product development requiring new approaches, e.g. consideration of interactions within its operating domain’s environments and other participants of the vehicle and its system, disruptive hardware technologies, human equivalent perception systems, exponentially growing software complexity, and an infinite number of road scenarios, etc. In order to deliver an optimal and robust product to customers, a very thorough Verification & Validation (V&V) must be performed under all of those infinite number of scenarios. While hardware-based development processes still remain as a crucial part of product development, such processes are no longer capable of meeting the exponentially growing V&V needs alone. As part of any effort to achieve a suitable V&V approach to AV/ADAS technology development it is necessary to identify the critical tasks to assemble such a V&V process and the scenario modeling and simulation capabilities that must be realized. Briefly, our goal is to support traditional Verification & Validation considerations, as well as to assess scenario coverage overall. What scenarios are to be considered? How might we define scenarios? How might we approach integration of scenario models and functional models in simulation? What is the expected output of these efforts? In this presentation, we will share those considerations in the hopes to drive further standardization and discussion across the industry, standards organizations, and tool vendors alike.
</p>
<br><br>
<p><strong>Digital Twin-Enabled Personalized Adaptive Cruise Control</strong></p>
<p><strong>Ziran Wang</strong>, Assistant Professor, Purdue University
<p><strong>Abstract</strong>
<br>
A Digital Twin is a digital replica of a living or nonliving physical entity, and this emerging technology attracted extensive attention from different industries during the past decade. In this talk, a Mobility Digital Twin (MDT) framework is developed, which is defined as an AI-based data-driven cloud-edge-device framework for mobility services. This MDT consists of three building blocks in the physical space (namely Human, Vehicle, and Traffic), and their associated Digital Twins in the digital space. The effectiveness of the MDT framework is shown through the case study of a personalized adaptive cruise control system, which leverages digital twins of driver, vehicle, and traffic information. This system learns from the naturalistic driving behavior and acts in a personalized manner to satisfy individual driver's preferences regarding car following.
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