REU Site: CAT Vehicle: The Cognitive and Autonomous Test Vehicle

这个项目是对亚利桑那大学本科生研究经验(REU)网站的更新。该项目的目标是使学生能够参与到与自动地面车辆和机器学习相关的无数应用程序中。该方法是基于模型的设计方法的应用，该方法提高了抽象水平，以允许全尺寸机器人车辆试验台的安全操作。这个为期10周的网站将为10名NSF资助的本科生提供身临其境的研究体验，他们与研究生并肩坐在一起，致力于当今最引人注目、最复杂的应用之一：自主系统。在全国范围内招募的一群不同的学生将为他们的研究做出令人兴奋的最终演示。一辆全尺寸的机器人汽车将与机器学习的核心研究配对，通过汽车传感器收集的数据成为可能。这些主题提供了一个背景，参与者将在其中探索基于模型的设计研究，用于网络物理系统、机器学习、人在环中系统、控制和自主系统。作为全尺寸车辆试验台安全程序的一部分，参与者将使用螺旋式开发过程，在验证之前的要求后添加新的项目要求。通过这些流程结构，参与者将留下他们项目的数据集、软件和视频演示。该项目将提高工作的重复性，并提供有益于最先进技术的经验记录，以及证明基于模型的方法可以增强本科生研究人员在发现方面的潜力的文物。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project is a renewal of a Research Experiences for Undergraduates (REU) Site at the University of Arizona. The goal of the project is to empower students to engage with the myriad applications that are related to autonomous ground vehicles and machine learning. The approach is the application of model-based design approaches that raise the level of abstraction to permit the safe operation of a full-sized robotic vehicle testbed. The 10-week Site will provide 10 NSF-funded undergraduate students with immersive research experiences, sitting side-by-side with graduate researchers and working on one of the most compelling, and complex applications of today: autonomous systems. A diverse group of students recruited nationwide will produce an exciting final demonstration of their research.A full-size robotic car will be paired with core research in machine learning, made possible with data gathered with automotive sensors. These themes provide a context in which participants will explore research in model-based design for cyber-physical systems, machine learning, human-in-the-loop systems, control, and autonomous systems. Participants will use a spiral development process, where new project requirements are added after previous requirements are verified, as part of the safety procedures for the full-sized vehicle testbed. Through these process structures, participants will leave behind datasets, software, and video demonstrations of their projects. This project will improve the reproducibility of the work and provide records of the experience to benefit the state-of-the-art, and artifacts that demonstrate the potential that model-based approaches can empower undergraduate researchers in discovery.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.