Despite the growing importance of autonomous driving (AD) education, educators face substantial challenges in providing undergraduate students with accessible, hands-on learning experiences that connect theory and practice. AD education requires students to master complex, interdisciplinary systems, yet traditional teaching methods often fail to fully integrate theory and practice. Various strategies, such as simulation environments and small-scale autonomous vehicle platforms, have been explored to address these challenges. However, these approaches often lack the realism or scalability required to provide students with a comprehensive understanding of AD systems. To address these challenges, we developed and evaluated a competition-based learning framework designed to teach AD software technology. In a three-year iterative design study, we implemented a framework combining a cost-effective 1/5-scaled autonomous vehicle platform with team-based competitions. This framework was progressively improved through a mixed-methods analysis of data from 445 students across five competitions and two capstone design courses. The quantitative results showed statistically significant improvements in the students’ self-assessed skills with large effect sizes, demonstrating that the framework significantly boosted students’ technical abilities and motivation. The qualitative feedback confirmed the educational value of the platform’s realism and peer-based observations of the competitions. This article presents the complete educational framework, outlines the iterative design process and key design decisions, and discusses the lessons learned. It also provides a validated model and practical guidelines for educators seeking to incorporate effective, competition-based AD education into their curricula.