What Will You Learn?

• Introduction to Autonomous Vehicles: Understand the fundamentals, history, and evolution of autonomous vehicle technology.
• Sensor Technologies: Learn about the various sensors used in autonomous vehicles, including LIDAR, RADAR, cameras, and ultrasonic sensors.
• Perception and Environment Modeling: Study techniques for object detection, classification, and tracking to create an accurate model of the vehicle's surroundings.
• Localization and Mapping: Gain insights into algorithms for determining a vehicle's precise location and building real-time maps (SLAM - Simultaneous Localization and Mapping).
• Path Planning and Decision Making: Explore methods for route planning, obstacle avoidance, and making driving decisions in complex environments.
• Control Systems: Understand the principles of vehicle dynamics and the design of control systems for steering, acceleration, and braking.
• Machine Learning and AI in Autonomous Vehicles: Apply machine learning algorithms and neural networks for perception, decision-making, and predictive analytics.
• Simulation and Testing: Learn to use simulation tools and frameworks for developing, testing, and validating autonomous driving systems.
• Safety and Redundancy: Study the safety protocols, redundancy measures, and fail-safe mechanisms crucial for autonomous vehicle operations.
• Regulatory and Ethical Considerations: Understand the legal, ethical, and regulatory aspects of deploying autonomous vehicles in the real world.
• Hardware and Software Integration: Gain practical skills in integrating hardware components and developing software for autonomous vehicle systems.
• Hands-On Projects and Capstone: Engage in practical projects and a capstone project to apply theoretical knowledge to real-world autonomous driving scenarios.