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

Lawrence Technological University, Southfield MI

Investigators

Abstract

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.