AI in Autonomous Vehicles

Introduction to the Course

The AI in Autonomous Vehicles Course helps learners understand, create and apply artificial intelligence (AI) technologies used in autonomous vehicles and intelligent transportation systems (ITS). Autonomous vehicles rely heavily on AI and machine learning to understand their environment and make safe driving decisions as well as computer vision technology to see the road ahead and to make real-time driving decisions. In this course you will review how AI allows an autonomous vehicle to interpret sensor data, detect objects, plan a route and make driving decisions in unpredictable and ever-changing environments. The AI course has been created to provide a strong foundation of theory with hands-on experiences that will prepare learners to address real-world autonomous driving challenges.

Course Objectives

• After completing this AI in Autonomous Vehicles Course, you will be able to:
• Familiarize yourself with the fundamental concepts of AI in autonomous vehicles and the use of autonomy stacks for self-driving cars.
• Understand how ADAS functions incorporate elements of machine learning, perception, and control.
• Developing perception models and testing them against scenarios to gain practical experience.
• Mastering the fundamentals of sensor fusion to effectively utilize multi-sensor information and derive reliable scene understanding.
• Understand planning and decision-making pipelines to transform perception outputs to safe actions.

What Will You Learn (Modules)

Module 1: Introduction to Robotics and Kinematics

• Overview of robotics: history, types, and real-world applications.
• Basic kinematics: motion, coordinate systems, and robot arm movements.
• Introduction to robot design: sensors, actuators, and control systems.
• Hands-on project: Build a basic robotic arm model and simulate simple movements.

Module 2: Robot Control Systems and Motion Planning

• Understanding control systems in robotics: PID controllers, feedback loops, and motion planning algorithms.
• How to control robotic movements with precision and accuracy.
• Explore motion planning algorithms for pathfinding and navigation.
• Hands-on project: Program a simple robot to follow a pre-defined path using a PID controller.

Module 3: AI in Robotics: Machine Learning and Computer Vision

• Introduction to AI in robotics: applying machine learning to improve robot behavior.
• Understanding computer vision: how robots perceive and interpret their environment.
• Using AI for object detection, recognition, and tracking in robotic systems.
• Hands-on project: Implement a simple computer vision algorithm to detect objects using a camera sensor.

Module 4: Autonomous Robots and Reinforcement Learning

• Introduction to autonomous robots: self-navigation, decision-making, and task execution.
• Exploring reinforcement learning for autonomous decision-making and robot training.
• Designing and simulating intelligent robots that learn from their environment.
• Hands-on project: Train a robot to navigate an environment using reinforcement learning algorithms.

Final Project

• Design and implement a robot capable of performing a specific task autonomously (e.g., obstacle avoidance, object sorting, or delivery).
• Integrate sensors and AI algorithms for real-time decision-making and autonomous control.
• Present the final project, including code implementation and simulation results.

Who Should Take This Course?

This course is ideal for:

• AI and Machine Learning Engineers interested in autonomous systems.
• Automotive Professionals seeking to transition into AI-driven vehicle technologies.
• Robotics Engineers working on intelligent mobility solutions.
• Data Scientists aiming to apply AI to real-time, sensor-based systems.
• Students and Researchers in AI, robotics, and transportation.
• Technology Enthusiasts curious about self-driving cars and future mobility.

Job Oppurtunities

Learners completing this course can pursue roles such as:

• Autonomous Vehicle Engineer: Developing AI systems for self-driving cars.

• Computer Vision Engineer: Building perception systems for autonomous vehicles.

• AI Robotics Engineer: Designing intelligent navigation and control systems.

• Machine Learning Engineer (Autonomous Systems): Training and optimizing driving models.

• ADAS Engineer: Working on advanced driver-assistance systems.

• Simulation and Testing Engineer: Validating autonomous vehicle performance.

Why Learn With Nanoschool?

Nanoschool provides you with real experience with autonomous vehicle technology by giving you direct experience working in the industry.

Advantages of Training at Nanoschool:

• Train under Experts: You will learn from people who have worked in the AI field, robotics, automotive systems, etc.
• Start Learning Now: You will use real-world datasets and simulate using AI frameworks.
• Keep Up To Date: Your curriculum is aligned to reflect current trends and technologies related to the autonomous vehicle industry.
• Career Support: We provide you with resources, workshops, and other ways to assist in your pursuit of a career or job within the AI/Automotive Domain.

Key outcomes of the course

Upon Completion of this Course, a Learner Will Be Able to:

• Create, Design and Construct AI Models used for the Purpose of Building Autonomous Vehicles using AI Technology.
• Ability to Use Various Sensors, Simulation Tools, and Actual Real-Time Autonomous Vehicle Data.
• Ability to Work with an Start-up, Automotive Company and/ or Mobility Company as a Workplace Contributor to Mechatronic Systems Related to Autonomous Vehicles.
• Utilize AI in a Manner that is Acceptable from a Safety, Ethical and Regulatory Standpoint.

Join Us and View How AI is Changing the World of Autonomous Vehicles. Develop and Build Your Skillset Related to the Future of Intelligent Transportation and Autonomous Vehicle Engineering.