AI and Deep Learning for Autonomous Driving

• Proficiency in Python programming
• Experience with machine learning and deep learning frameworks
• Familiarity with automotive technology and computer vision

Audience

• Data scientists aiming to work on autonomous driving applications
• AI specialists focusing on automotive AI development
• Developers interested in deep learning techniques for self-driving cars

AI and Deep Learning for Autonomous Driving is a specialized course focused on leveraging AI algorithms and deep learning techniques to develop autonomous vehicle functionalities.

This instructor-led, live training (online or onsite) is aimed at advanced-level data scientists, AI specialists, and automotive AI developers who wish to build, train, and optimize AI models for autonomous driving applications.

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

• Understand the fundamentals of AI and deep learning in the context of autonomous vehicles.
• Implement computer vision techniques for real-time object detection and lane following.
• Utilize reinforcement learning for decision-making in self-driving systems.
• Integrate sensor fusion techniques for better perception and navigation.
• Build deep learning models to predict and analyze driving 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 AI in Autonomous Vehicles

• Understanding autonomous driving levels and AI integration
• Overview of AI frameworks and libraries used in autonomous driving
• Trends and innovations in AI-powered vehicle autonomy

Deep Learning Fundamentals for Autonomous Driving

• Neural network architectures for self-driving cars
• Convolutional neural networks (CNNs) for image processing
• Recurrent neural networks (RNNs) for temporal data

Computer Vision for Autonomous Driving

• Object detection using YOLO and SSD
• Lane detection and road following techniques
• Semantic segmentation for environmental perception

Reinforcement Learning for Driving Decisions

• Markov Decision Processes (MDP) in autonomous vehicles
• Training deep reinforcement learning (DRL) models
• Simulation-based learning for driving policies

Sensor Fusion and Perception

• Integrating LiDAR, RADAR, and camera data
• Kalman filtering and sensor fusion techniques
• Multi-sensor data processing for environment mapping

Deep Learning Models for Driving Prediction

• Building behavioral prediction models
• Trajectory forecasting for obstacle avoidance
• Driver state and intent recognition

Model Evaluation and Optimization

• Metrics for model accuracy and performance
• Optimization techniques for real-time execution
• Deploying trained models in autonomous vehicle platforms

Case Studies and Real-World Applications

• Analyzing autonomous vehicle incidents and safety challenges
• Exploring successful implementations of AI-driven driving systems
• Project: Developing a lane-following AI model

Summary and Next Steps