This paper presents a tutorial on constructive safety-critical control for autonomous systems using reduced-order models (ROMs) and control barrier functions (CBFs). The authors highlight the challenges of designing controllers for high-dimensional nonlinear dynamics in modern autonomous systems, such as flying, legged, and wheeled robots. They introduce the concept of CBFs, which are scalar functions whose time derivative depends on the system's control input, and their zero superlevel set defines a controlled invariant subset of the state space. The paper reviews the theoretical foundations of CBFs, including set invariance, barrier functions, and input-to-state safety (ISSf). It also discusses various types of CBFs, such as robust, adaptive, data-driven, and stochastic CBFs, and their applications in different control scenarios. The primary focus of the paper is on constructing CBFs for complex systems using ROMs, which are lower-dimensional representations that capture the high-level behavior of the full-order system. The authors provide a unified formulation of techniques that construct CBFs for complex systems from simpler CBFs for ROMs. They illustrate these techniques through formal results, numerical examples, and case studies of real-world systems, including fixed-wing aircraft, flying, legged, and wheeled robots, manipulators, and heavy-duty trucks. The paper also discusses the limitations of the presented paradigms and suggests open research directions. It aims to provide a comprehensive methodology for systematically constructing CBFs for high-dimensional nonlinear systems, enabling the design of safe and efficient controllers for complex autonomous systems.This paper presents a tutorial on constructive safety-critical control for autonomous systems using reduced-order models (ROMs) and control barrier functions (CBFs). The authors highlight the challenges of designing controllers for high-dimensional nonlinear dynamics in modern autonomous systems, such as flying, legged, and wheeled robots. They introduce the concept of CBFs, which are scalar functions whose time derivative depends on the system's control input, and their zero superlevel set defines a controlled invariant subset of the state space. The paper reviews the theoretical foundations of CBFs, including set invariance, barrier functions, and input-to-state safety (ISSf). It also discusses various types of CBFs, such as robust, adaptive, data-driven, and stochastic CBFs, and their applications in different control scenarios. The primary focus of the paper is on constructing CBFs for complex systems using ROMs, which are lower-dimensional representations that capture the high-level behavior of the full-order system. The authors provide a unified formulation of techniques that construct CBFs for complex systems from simpler CBFs for ROMs. They illustrate these techniques through formal results, numerical examples, and case studies of real-world systems, including fixed-wing aircraft, flying, legged, and wheeled robots, manipulators, and heavy-duty trucks. The paper also discusses the limitations of the presented paradigms and suggests open research directions. It aims to provide a comprehensive methodology for systematically constructing CBFs for high-dimensional nonlinear systems, enabling the design of safe and efficient controllers for complex autonomous systems.