This paper addresses the challenge of designing controllers for safety-critical systems that balance conflicting control objectives and safety constraints. It introduces a methodology that unifies control barrier functions (CBFs) and control Lyapunov functions (CLFs) within a real-time optimization-based controller framework. The key contributions include: 1. **Unified Framework**: The paper develops a unified framework that integrates safety conditions (expressed as CBFs) and performance objectives (expressed as CLFs) in the context of real-time optimization-based controllers. 2. **CBF and CLF Definitions**: It formulates conditions on the derivative of barrier functions (reciprocal and zeroing) that are minimally restrictive on the interior of the set, allowing for a larger set of compatible control inputs. 3. **Control Barrier Functions (CBFs)**: The paper defines reciprocal and zeroing control barrier functions (RCBFs and ZCBFs) and their properties, showing that the existence of a ZBF implies forward invariance of the set. 4. **Quadratic Programs (QPs)**: The paper introduces QPs that mediate the tradeoff between achieving a stabilization objective (represented by CLFs) and ensuring the system remains in a safe set (represented by CBFs). This allows for the unification of multiple control objectives and constraints. 5. **Illustrative Examples**: The methodology is demonstrated on two automotive control problems: Adaptive Cruise Control (ACC) and Lane Keeping (LK), showcasing the effectiveness of the proposed approach in balancing safety and performance. The paper provides a comprehensive theoretical foundation and practical examples to illustrate the application of the proposed methodology in safety-critical systems.This paper addresses the challenge of designing controllers for safety-critical systems that balance conflicting control objectives and safety constraints. It introduces a methodology that unifies control barrier functions (CBFs) and control Lyapunov functions (CLFs) within a real-time optimization-based controller framework. The key contributions include: 1. **Unified Framework**: The paper develops a unified framework that integrates safety conditions (expressed as CBFs) and performance objectives (expressed as CLFs) in the context of real-time optimization-based controllers. 2. **CBF and CLF Definitions**: It formulates conditions on the derivative of barrier functions (reciprocal and zeroing) that are minimally restrictive on the interior of the set, allowing for a larger set of compatible control inputs. 3. **Control Barrier Functions (CBFs)**: The paper defines reciprocal and zeroing control barrier functions (RCBFs and ZCBFs) and their properties, showing that the existence of a ZBF implies forward invariance of the set. 4. **Quadratic Programs (QPs)**: The paper introduces QPs that mediate the tradeoff between achieving a stabilization objective (represented by CLFs) and ensuring the system remains in a safe set (represented by CBFs). This allows for the unification of multiple control objectives and constraints. 5. **Illustrative Examples**: The methodology is demonstrated on two automotive control problems: Adaptive Cruise Control (ACC) and Lane Keeping (LK), showcasing the effectiveness of the proposed approach in balancing safety and performance. The paper provides a comprehensive theoretical foundation and practical examples to illustrate the application of the proposed methodology in safety-critical systems.