Computational Techniques for Autonomous Vehicles is a specialized course that introduces the mathematical, statistical, and computational foundations required for the development of intelligent autonomous vehicle systems. The course focuses on the essential computational methods used in perception, localization, mapping, navigation, motion planning, decision-making, and control of autonomous vehicles and mobile robots.The course begins with the fundamentals of linear algebra and geometry, including vector spaces, matrix operations, coordinate transformations, eigenvalues, and eigenvectors, which are widely used in sensor fusion and perception systems. Learners will also study calculus and optimization techniques applied in motion analysis, trajectory generation, path planning, and optimal control problems. Concepts such as gradients, derivatives, curvature analysis, and optimization algorithms are explored with practical relevance to autonomous driving applications. In addition, the course covers probability, statistics, and uncertainty management techniques including probability distributions, Bayesian inference, Gaussian processes, Hidden Markov Models, and Monte Carlo localization methods. Numerical methods such as interpolation, numerical integration, differentiation, and approximation techniques for solving differential equations are also introduced. The course further explores graph theory and network analysis concepts including shortest path algorithms, graph-based SLAM, and road network representation. Through practical examples, simulations, and real-world case studies, learners will develop strong analytical and computational skills required for modern autonomous vehicle and intelligent transportation technologies.