Bilevel optimization is a foundational tool for modeling hierarchical decision-making problems, becoming increasingly prevalent in various fields including machine learning, transportation, energy systems, and robotics. However, existing research on bilevel optimization often focuses on simplified or unconstrained settings, limiting its applicability to practical scenarios that require safe, real-time decision-making under complex constraints. This project aims to bridge this gap by developing efficient and scalable algorithms for constrained, nonconvex bilevel problems, with particular emphasis on planning and control tasks in safety-critical domains. The outcomes of this research will provide general-purpose optimization tools that benefit a broad range of applications in robotics, learning, and autonomous systems. Software developed through this project will be released as open-source packages, enabling broad adoption across academia and industry. Furthermore, this project includes a comprehensive educational and outreach component to engage students at multiple academic levels, foster hands-on learning experiences, and broaden participation in STEM fields. This project advances the theory and practice of bilevel optimization by designing efficient, safe, and scalable algorithms tailored specifically to constrained, nonconvex bilevel problems in planning and control. The research comprises two main thrusts. In the first thrust, a novel control-theoretic framework will b