Computer Vision for Autonomous Driving

• Proficiency in Python programming
• Basic understanding of machine learning concepts
• Familiarity with image processing techniques

Audience

• AI developers working on autonomous driving applications
• Computer vision engineers focusing on real-time perception
• Researchers and developers interested in automotive AI

Computer Vision for Autonomous Driving is a comprehensive course designed to teach developers how to implement computer vision techniques for perception and environment understanding in autonomous vehicles.

This instructor-led, live training (online or onsite) is aimed at intermediate-level AI developers and computer vision engineers who wish to build robust vision systems for autonomous driving applications.

By the end of this training, participants will be able to:

• Understand the fundamental concepts of computer vision in autonomous vehicles.
• Implement algorithms for object detection, lane detection, and semantic segmentation.
• Integrate vision systems with other autonomous vehicle subsystems.
• Apply deep learning techniques for advanced perception tasks.
• Evaluate the performance of computer vision models in real-world scenarios.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Computer Vision in Autonomous Driving

• Role of computer vision in autonomous vehicle systems
• Challenges and solutions in real-time vision processing
• Key concepts: object detection, tracking, and scene understanding

Image Processing Fundamentals for Autonomous Vehicles

• Image acquisition from cameras and sensors
• Basic operations: filtering, edge detection, and transformations
• Preprocessing pipelines for real-time vision tasks

Object Detection and Classification

• Feature extraction using SIFT, SURF, and ORB
• Classical detection algorithms: HOG and Haar cascades
• Deep learning approaches: CNNs, YOLO, and SSD

Lane and Road Marking Detection

• Hough Transform for line and curve detection
• Region of interest (ROI) extraction for lane marking
• Implementing lane detection using OpenCV and TensorFlow

Semantic Segmentation for Scene Understanding

• Understanding semantic segmentation in autonomous driving
• Deep learning techniques: FCN, U-Net, and DeepLab
• Real-time segmentation using deep neural networks

Obstacle and Pedestrian Detection

• Real-time object detection with YOLO and Faster R-CNN
• Multi-object tracking with SORT and DeepSORT
• Pedestrian recognition using HOG and deep learning models

Sensor Fusion for Enhanced Perception

• Combining vision data with LiDAR and RADAR
• Kalman filtering and particle filtering for data integration
• Improving perception accuracy with sensor fusion techniques

Evaluation and Testing of Vision Systems

• Benchmarking vision models with automotive datasets
• Real-time performance evaluation and optimization
• Implementing a vision pipeline for autonomous driving simulation

Case Studies and Real-World Applications

• Analyzing successful vision systems in autonomous cars
• Project: Implementing a lane and obstacle detection pipeline
• Discussion: Future trends in automotive computer vision

Summary and Next Steps