Sensor Fusion and Non-Linear Filtering for Automotive Systems

Automated vehicles rely on highly accurate perception to navigate safely, requiring the seamless integration of noisy data from cameras, radar, and lidar. Understanding how to combine these diverse sensor inputs using mathematical filtering is an essential skill for modern automotive and robotics engineers. This text-based course guides you through the foundational mathematics and algorithmic implementations of sensor fusion. You will transition from basic probability concepts to understanding how robust non-linear filters track vehicles and pedestrians in real-world scenarios. What you'll learn: - Understand the foundational concepts of Bayesian state estimation and sensor fusion architectures. - Implement Extended and Unscented Kalman Filters for tracking dynamic objects with non-linear motion. - Apply particle filtering techniques to handle highly complex, non-Gaussian noise environments. - Analyze the trade-offs between early and late sensor fusion methodologies using radar and lidar paradigms. - Practice designing state estimation algorithms through structured, step-by-step written code walkthroughs. The course begins with core probability theory, coordinate systems, and motion models before progressing to advanced non-linear filtering algorithms and practical automotive tracking scenarios. You will study detailed explanations and analyze clean code implementations to solidify your technical understanding. This course is designed for beginners to automotive perception, software developers, and engineering students. A basic background in linear algebra and programming is recommended, but no prior sensor fusion experience is required. Start reading today to build the mathematical foundation needed for modern autonomous vehicle perception.