REU Site: Collaborative Research: Developing, Analyzing, and Evaluating Self-drive Algorithms Using Real Street Legal Electric Vehicles on Campus

REU 网站：合作研究：在校园内使用真实街道合法电动汽车来开发、分析和评估自动驾驶算法

• 批准号: 2150292
• 负责人: Chan-Jin Chung
• 金额: $ 28.17万
• 依托单位: Lawrence Technological University
• 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: 2022-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2150292&HistoricalAwards=false
• 关键词: REU; Site; Collaborative; Research; Developing

This project provides hands-on active learning opportunities for undergraduate students to conduct research for urban road self-driving functions using street-legal vehicles. It is uncommon for US undergraduate students to have opportunities to develop self-drive algorithms using real vehicles. In this research, students will analyze and evaluate the results of different algorithms after real-world testing. They will gain knowledge and confidence in self-driving algorithm development, share their knowledge with others through publications, and potentially choose autonomous vehicle research and development as their career path. Student perceptions about the smart mobility field and career options will be positively affected, leading to increased interest in future graduate studies. Evaluation and comparison results of various self-driving algorithms tested are original contributions to the research community. The results will benefit the advance of autonomous vehicle software development, bringing benefits to society including reduced pollution, less traffic accidents and congestion, and lower economic costs.The specific objectives are to: (1) provide experiences to underrepresented undergraduate students who otherwise might not have research opportunities to learn fundamental theories in autonomous vehicle development; (2) allow students to design algorithms to practice software development using real vehicles on real test courses; (3) strengthen their confidence, self-guided capabilities, and research skills; and, (4) increase the number of students interested in graduate programs and ultimately provide a quality research and development workforce to industry. Activities include (1) intensive daily training workshops with hands-on problem solving tasks with real vehicles; (2) defining research problems; (3) design, implementation, and testing of algorithms; (4) analysis and evaluation of the collected data and results; (5) writing technical reports and presentations; (6) assessments before and after the program; (7) field trips; and (8) post-meetings for publications. This research also advances knowledge by identifying advantages and disadvantages of using real vehicles in teaching and learning self-driving algorithms, plus the most effective strategies to teach self-driving algorithms using real vehicles in order to improve undergraduate education.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.

该项目为本科生提供动手积极学习的机会，以使用街头法律车辆进行城市道路自动驾驶功能的研究。对于我们本科生来说，有机会使用真正的车辆开发自动驾驶算法并不常见。在这项研究中，学生将在现实世界测试后分析和评估不同算法的结果。他们将获得对自动驾驶算法开发的知识和信心，通过出版物与他人分享他们的知识，并有可能选择自动驾驶汽车研发作为其职业道路。学生对智能出行领域和职业选择的看法将受到积极影响，从而增加对未来研究生学习的兴趣。测试的各种自动驾驶算法的评估和比较结果是对研究界的原始贡献。结果将使自动驾驶汽车软件开发的发展有益，从而为社会带来利益，包括减少污染，交通事故和股票降低以及经济成本降低。具体目标是：（1）为代表性不足的本科生提供经验，这些学生否则可能没有研究自动驾驶汽车开发中基本理论的研究机会； （2）允许学生设计算法，可以在实际测试课程上使用实际车辆练习软件开发； （3）增强他们的信心，自我指导的能力和研究技能； （4）增加对研究生课程感兴趣的学生人数，并最终为行业提供优质的研发劳动力。活动包括（1）使用真实车辆的动手解决问题的强化日常培训讲习班； （2）定义研究问题； （3）算法的设计，实现和测试； （4）分析和评估收集的数据和结果； （5）编写技术报告和演示； （6）计划前后的评估； （7）实地考察； （8）出版物的邮政。这项研究还通过确定在教学和学习自动驾驶算法中使用真正的车辆的优势和缺点来提高知识，以及最有效的策略，是使用实际工具来教授自动驾驶算法以改善本科教育的最有效的策略。该奖项反映了NSF的法定任务，并通过评估了基金会的智力效果，并被视为与基金会的构成效果相值。

项目成果

Developing, Analyzing, and Evaluating Self-Drive Algorithms Using Electric Vehicles on a Test Course

在测试场上开发、分析和评估使用电动汽车的自动驾驶算法

• DOI: 10.1109/mass56207.2022.00101
• 发表时间: 2022
• 期刊: 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS
• 作者: Kaddis, Ryan;Stading, Enver;Bhuptani, Aarna;Song, Heather;Chung, Chan-Jin;Siegel, Joshua
• 通讯作者: Siegel, Joshua

Developing, Analyzing, and Evaluating Vehicular Lane Keeping Algorithms Using Electric Vehicles

开发、分析和评估使用电动汽车的车辆车道保持算法

• DOI: 10.3390/vehicles4040055
• 发表时间: 2022
• 期刊: Vehicles
• 影响因子: 2.2
• 作者: Rao, Shika;Quezada, Alexander;Rodriguez, Seth;Chinolla, Cebastian;Chung, Chan-Jin;Siegel, Joshua
• 通讯作者: Siegel, Joshua