Undergraduate Research Experiences for Automated and Connected Vehicle Algorithm Development using Real Vehicles

We are experiencing a revolution in vehicle operation, with fully automated robotaxis deployed and available for public use in major U.S. markets in 2023. These vehicles, while imperfect, already are arguably safer than the average human driver. Despite this rapid progress, there remain significant research and development problems that must be addressed; beyond this, there is an underdeveloped workforce for skilled researchers, developers, and practitioners in these areas, a fact that may delay necessary advances. We have created and run for two years a National Science Foundation funded Research Experience for Undergraduates (NSF REU) focused on solving both unmet research needs, and workforce development and pipeline programs. In our REU, which makes use of simulation and two full-scale, street-legal drive-by-wire electric vehicles with perception, planning, and control capabilities, our primary goals include to (1) provide hands-on experiences to 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 and evaluation using real vehicles on real test courses, (3) strengthen their confidence, self-guided capabilities, and research skills, and (4) increase the number of students, including those from diverse backgrounds and technical disciplines, interested in graduate programs to ultimately provide a quality research and development workforce to both academia and industry. Over the initial two years, a cohort of 8 diverse students each year learned fundamental self-driving and networking skills including coding for drive-by-wire vehicles, computer vision, use of localization, and interpretation of richer sensor data, as well as network and communication protocols. The students were introduced to research ideation and publishing concepts, mentored in designing and testing hypotheses, and then involved in two challenges related to self-driving and networked vehicles. Two teams of 4 designed, implemented, tested various self-drive and V2X algorithms using real vehicles on a test course, analyzed/evaluated test results, wrote technical reports, and delivered presentations. After the summer program was over, the technical reports were published in peer reviewed conferences and journals. Survey results show that students attained significant & real-world computer science skills in autonomous vehicle development leveraging real vehicles available. The programs also increased research career interests and strengthened students' confidence, self-guided capabilities, and research skills, while additionally supporting the development of workshop materials, simulators, and related content that provide valuable resources for others planning to develop an undergraduate curriculum to teach self-drive and networked vehicle development.