Motion Planning for Self-Driving Cars: Algorithms and Behavior

Developing software for autonomous vehicles requires a deep understanding of how a car decides where to go and how to get there safely. This course guides you through the core concepts of motion planning, translating environmental data into safe, actionable driving maneuvers. You will transition from understanding basic navigation to designing structured decision-making systems for self-driving cars. By reading through detailed explanations and studying algorithmic logic, you will learn how autonomous systems plan long-range missions, handle dynamic traffic scenarios, and calculate smooth, collision-free paths in real time. What you'll learn: - Understand the core architecture of autonomous motion planning, from global routing to local trajectory generation. - Apply classic graph-search algorithms like Dijkstra’s and A* to find optimal paths through road networks. - Design behavior-planning logic using finite state machines to handle intersections, lane changes, and pedestrian crossings. - Formulate local trajectory planners that generate smooth, executable steering and acceleration profiles. - Explore modern path-planning concepts such as Hybrid A* and safety-critical collision avoidance constraints. The curriculum begins with essential terminology and the hierarchical breakdown of the planning problem, before moving step-by-step into graph search, behavioral decision-making, and continuous trajectory optimization. You will analyze written algorithmic examples and pseudocode designed to build practical engineering intuition. This course is designed for aspiring robotics engineers, software developers, and tech enthusiasts who are new to autonomous vehicle technology. No prior experience in self-driving systems is required, though a basic familiarity with programming concepts will help you get the most out of the material. Start reading today and build the foundational knowledge to program the vehicles of tomorrow.