MRI: Acquisition of Autonomous Plug-In Hybrid Vehicle Platform for Multidisciplinary Research and Education at the University of Michigan-Dearborn

MRI：收购密歇根大学迪尔伯恩分校用于多学科研究和教育的自主插电式混合动力汽车平台

• 批准号: 2214830
• 负责人: Jaerock Kwon
• 金额: $ 24.46万
• 依托单位: Regents of the University of Michigan - Dearborn
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2214830&HistoricalAwards=false
• 关键词: MRI; Acquisition; Autonomous; Plug; Hybrid

This NSF MRI project aims to acquire a high-performance autonomous electric vehicle platform with a sensor suite for research and education to advance fundamental science and engineering research and education. The intellectual merits of the project include the following. The platform will accelerate the development of critical algorithms in machine learning and analysis methods tailored to the safety and stability of autonomous vehicles while enabling transformative research on cybersecurity by providing real-world scenarios. Research on energy systems for advanced mobility will also be able to be extended and further explored. The broader impacts of the project entail the following. The platform will support undergraduate and graduate students, as well as post-doctoral fellows by offering research training opportunities through experiential learning with a programmable electric vehicle. The University of Michigan-Dearborn (UM-D) is located in the Metro-Detroit area, the home to the “Big Three” (GM, Ford, and Chrysler), and automotive suppliers. The U.S. automotive and advanced mobility industries need more skilled and knowledgeable scientists and engineers who are ready for new technologies such as intelligent systems powered by artificial intelligence and machine learning, energy and power systems, cybersecurity, and human-vehicle interfaces. The project will help in contributing to the high demand for skilled workers from the advanced mobility industry with the acquired instrument. The platform will be crucial research instrumentation to significantly enhance interdisciplinary research and education at UM-D in several research activities, including embodied cognitive vehicle, in-vehicular network security, energy consumption, environmental perception, cybersecurity, and driver behavior analyses in electric and advanced mobilities. The instrument will also substantially improve undergraduate and graduate research training in the electrical, computer, robotics, mechanical, and industrial engineering departments at UM-D. Active research is going on in the fields of automotive, robotics, cybersecurity, energy systems, and human-vehicle interface at UM-D. The proposed platform will enable collaborative research in a realistic environment with a full-scale programmable vehicle in the aforementioned emerging research areas. The project team will work on ten transformative research topics to be enabled by the platform that will substantially improve the current research and experimentation capabilities at UM-D.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.

该 NSF MRI 项目旨在获得一个高性能自动驾驶电动汽车平台，该平台配有用于研究和教育的传感器套件，以推进基础科学和工程研究和教育。该项目的智力优点包括以下内容。该平台将加速机器学习和分析方法中关键算法的开发，以适应自动驾驶汽车的安全性和稳定性，同时通过提供真实场景来实现网络安全的变革性研究。对先进移动能源系统的研究也将能够得到扩展和进一步探索。该项目更广泛的影响如下。该平台将通过可编程电动汽车的体验式学习提供研究培训机会，为本科生、研究生以及博士后提供支持。密歇根大学迪尔伯恩分校 (UM-D) 位于底特律都会区，这里是“三巨头”（通用汽车、福特和克莱斯勒）和汽车供应商的所在地。美国汽车和先进移动行业需要更多技术精湛、知识渊博的科学家和工程师，他们要为新技术做好准备，例如人工智能和机器学习驱动的智能系统、能源和电力系统、网络安全以及人车界面。该项目将有助于利用所收购的仪器满足先进移动行业对技术工人的高需求。该平台将成为重要的研究工具，可显着加强密西根大学发展部多项​​研究活动的跨学科研究和教育，包括实体认知车辆、车载网络安全、能源消耗、环境感知、网络安全以及电动和先进移动出行中的驾驶员行为分析。该仪器还将大大改善密西根大学电气、计算机、机器人、机械和工业工程系的本科生和研究生研究培训。 UM-D 正在汽车、机器人、网络安全、能源系统和人车界面领域进行积极的研究。拟议的平台将能够在上述新兴研究领域使用全尺寸可编程车辆在现实环境中进行协作研究。该项目团队将致力于该平台支持的十个变革性研究课题，这将大大提高密西根大学迪尔分校当前的研究和实验能力。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

OPEMI: Online Performance Evaluation Metrics Index for Deep Learning-Based Autonomous Vehicles

OPEMI：基于深度学习的自动驾驶汽车在线性能评估指标

• DOI: 10.1109/access.2023.3246104
• 发表时间: 2023
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Kim, Donghyun;Khalil, Aws;Nam, Haewoon;Kwon, Jaerock
• 通讯作者: Kwon, Jaerock