Decision-Making Foundations for Autonomous Systems

How do self-driving cars and autonomous robots navigate complex, unpredictable environments safely? This text-based course guides you through the foundational logic, algorithms, and decision-making architectures that power modern autonomous systems. You will transition from understanding basic robotic concepts to grasping how autonomous agents perceive their surroundings, evaluate risks, and execute real-time decisions. Through clear written explanations, structured pseudo-code, and practical scenarios, you will learn to model decision pipelines for self-driving vehicles and robotic systems. What you'll learn: - Understand the fundamental architecture of autonomous decision-making systems. - Analyze sensor fusion concepts and how agents build environmental models. - Apply path-planning algorithms and behavior generation models to navigate static and dynamic obstacles. - Evaluate safety-critical decision frameworks and risk-assessment strategies. - Explore modern machine learning integrations and scenario-based testing workflows. The course begins with core definitions and architectural basics, progressing step-by-step through perception integration, path planning, and advanced safety validation techniques. It is designed for aspiring robotics engineers, software developers, and tech enthusiasts new to autonomous systems, requiring no prior experience in self-driving technology. Start reading today to master the logic behind autonomous navigation.