Sensor Fusion and Multi-Object Tracking for Self-Driving Systems

Understanding where surrounding obstacles are and where they are moving is the most critical challenge for self-driving vehicles. This text-based course guides you through the core mathematical concepts and algorithms that make autonomous navigation safe and reliable. You will transition from understanding raw sensor inputs to grasping how complex multi-object tracking systems operate in real-world traffic. By reading our structured explanations and studying clean Python code representations, you will gain the theoretical and practical knowledge needed to process and fuse data from multiple sensors. What you will learn: Understand the foundational principles of sensor fusion, coordinate systems, and spatial alignment; Apply Bayesian filtering techniques, focusing on Kalman Filters and Extended Kalman Filters; Implement data association algorithms to match new sensor measurements with existing object tracks; Explore modern multi-object tracking frameworks, including classic sorting and modern deep learning-assisted tracking concepts; Analyze how radar, lidar, and camera data are fused to create a single, unified view of a vehicle's surroundings. The course begins with essential terminology, basic probability, and the foundational mathematics of state estimation. You will then progress through step-by-step written walkthroughs of tracking algorithms, concluding with practical scenarios in self-driving environments. This course is designed for aspiring robotics engineers, software developers, and tech enthusiasts seeking a clear introduction to autonomous vehicle technology, with no prior sensor hardware experience required. Start reading today and build your foundation in modern autonomous perception systems.