Building a Self-Controlled Car through AI inferences & IoT
Building a Self-Controlled Car through AI inferences & IoT, Artificial Intelligence, automations, IoT, Self-driving, computer Vision, real-time detection, Arduino and Python.
Course Description
• Implement real-time object detection using YOLO and OpenCV
• Integrate IoT sensors (ultrasonic) for autonomous navigation
• Integrate live inference models on Arduino board
• Design control systems for steering, braking, and obstacle avoidance
• Build and test a mini self-driving car with Python-based control logic
Autonomous vehicles represent a transformative leap in transportation, driven by the convergence of computer vision, IoT, and real-time inference technologies. At the heart of this innovation lies computer vision, which enables vehicles to “see” and interpret their surroundings using cameras and deep learning models. Through techniques like object detection, lane tracking, and semantic segmentation, vehicles can identify pedestrians, traffic signs, and other vehicles with remarkable accuracy.
Complementing this is the Internet of Things (IoT), which connects a network of sensors—ultrasonic (UV sensors) and ESP32 camera and Arduino, that continuously stream data to the vehicle’s onboard systems. IoT not only enhances situational awareness but also enables vehicle-to-everything (V2X) communication, allowing cars to interact with infrastructure and other vehicles for coordinated movement.
For educators and developers, mastering these systems opens doors to innovation in smart cities, robotics, and industrial automation. This course empowers learners to explore that future hands-on, combining theory with practical projects that bring autonomous systems to life.
