Computational Techniques For Autonomous Vehicles
Published 5/2026
Created by Dr A Vimala Starbino
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 22 Lectures ( 10h 0m ) | Size: 4.4 GB

Mathematical Foundations and Algorithms for Intelligent Autonomous Systems
What you'll learn
⚡ Apply linear algebra and geometric computations to solve problems in coordinate transformations, localization, sensor fusion, and Kalman filtering.
⚡ Utilize calculus and optimization techniques to numerically analyze motion, compute trajectories, and solve path planning problems involving derivatives and cur
⚡ Employ probability and statistical methods to quantify uncertainty, perform estimation, and solve numerical problems in perception, localization, and decision-m
⚡ Implement numerical methods and graph-based algorithms to solve problems involving differential equations, root finding, interpolation, shortest path computatio
⚡ Students will learn how to apply mathematical and numerical techniques to solve real-world problems in autonomous vehicles, including modeling vehicle motion, a
Requirements
❗ Learners should have basic knowledge of mathematics, including algebra and elementary calculus. Familiarity with programming concepts using Python or MATLAB will be helpful for implementing computational algorithms and simulations. A basic understanding of physics, coordinate systems, and engineering mathematics is recommended. Prior exposure to robotics, artificial intelligence, machine learning, or autonomous systems is beneficial but not mandatory. Students should have access to a computer with internet connectivity for running simulations, coding exercises, and visualization tools. The course is designed to support beginners as well as learners interested in autonomous vehicles, robotics, intelligent transportation systems, and computational engineering applications.
Description
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.
Who this course is for
⭐ Basic understanding of high school mathematics, including algebra, geometry, and trigonometry. Familiarity with fundamental calculus concepts such as differentiation and integration. Introductory knowledge of probability and statistics is helpful but not mandatory. Ability to solve numerical problems and interpret mathematical results. No prior experience in autonomous vehicles is required; the course builds concepts from foundational principles.
⭐ This course is designed for undergraduate and postgraduate students, researchers, and professionals interested in autonomous vehicles, robotics, artificial intelligence, and intelligent transportation systems. It is suitable for learners from engineering, computer science, mechatronics, electronics, mechanical, and data science backgrounds who want to understand the computational and mathematical techniques behind autonomous navigation and control. The course is also beneficial for beginners seeking to build foundational knowledge in perception, localization, motion planning, sensor fusion, and autonomous system design using real-world computational approaches.
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