This course provides a comprehensive introduction to the advanced AI techniques used in autonomous driving systems. You will learn how self-driving cars perceive their surroundings, make decisions, and control motion using deep learning models built with TensorFlow. The program covers essential concepts like computer vision, object detection, lane tracking, sensor data processing, and reinforcement learning. With hands-on projects and real-world simulations, you’ll gain practical skills to design, train, and deploy intelligent driving models. By the end of the course, you will be able to build key components of an autonomous navigation system using state-of-the-art machine learning tools.
MODULE 1: Introduction to Self-Driving Cars Evolution of autonomous vehicles Levels of autonomy (SAE Levels 0–5) Key components: sensors, perception, planning, control Role of TensorFlow in autonomous systems Real-world use cases & industry overview.
MODULE 2: Python, TensorFlow & Deep Learning Foundations Python essentials for AI TensorFlow architecture Neural networks basics: activation, loss, optimization GPU environment setup Training/validation/test data workflow.
MODULE 3: Sensors & Data Acquisition Camera, LiDAR, RADAR, GPS, IMU basicsn Sensor fusion fundamentals Standard datasets: KITTI, Udacity, nuScenes Preprocessing pipeline for autonomous driving data.
MODULE 4: Computer Vision for Autonomous Driving Image classification using CNNs in TensorFlow Object detection (cars, lanes, pedestrians, traffic signs) Semantic segmentation models (U-Net / DeepLab) TensorFlow Object Detection API
MODULE 5: Lane Detection & Road Understanding Classical lane detection (Canny, Hough) Deep learning lane detection Road curvature & vehicle positioning TensorFlow implementation for real-time inference.
MODULE 6: Object Detection & Tracking YOLO / SSD / Faster R-CNN with TensorFlowm Multi-object tracking (DeepSORT / Kalman Filters) Occlusion handling Real-time detection optimization (TFLite, quantization).
MODULE 7: Sensor Fusion & Localization Kalman Filter & Extended Kalman Filter (EKF) Particle filters GPS + IMU + Camera fusion TensorFlow models for predicting position trajectories.
MODULE 8: Path Planning Algorithms Behavior planning (rule-based, learning-based) Route planning (A*, RRT, Dijkstra) Cost maps and motion constraints TensorFlow reinforcement learning for decision-making.
MODULE 9: Vehicle Control Systems PID controller for steering & speed Model Predictive Control (MPC) End-to-end driving with CNN + LSTM in TensorFlow Closed-loop control simulation.
MODULE 10: Autonomous Driving Project with TensorFlo Full Capstone Project
Build a real self-driving car pipeline using TensorFlow: Data collection & augmentation Lane + object detection model Localization + basic path planning Control logic in simulation Deployment using TensorFlow Lite / ROS.
Mobile: 9100348679Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â
Email: coursedivine@gmail.com
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