MATLAB and Simulink for Automated Driving Systems Training by Tonex
MATLAB and Simulink for Automated Driving Systems is a 2-day comprehensive course designed to equip professionals with the essential skills and knowledge required to excel in the field of automated driving systems development. Participants will gain hands-on experience in utilizing MATLAB and Simulink, industry-standard tools, to develop, simulate, and validate automated driving algorithms and systems.
By the end of this course, participants will have gained the expertise needed to excel in the dynamic field of automated driving systems using MATLAB and Simulink.
Learning Objectives:
Upon completion of this course, participants will:
- Learn the fundamentals of automated driving systems and their components.
- Proficiently use MATLAB and Simulink for algorithm development and testing.
- Gain insight into sensor fusion, perception, and control systems for autonomous vehicles.
- Learn to implement and validate automated driving algorithms in Simulink.
- Explore advanced features and capabilities of MATLAB and Simulink for autonomous driving.
- Acquire practical skills to enhance their career prospects in the rapidly evolving field of autonomous vehicles.
Audience:
This course is ideal for:
- Automotive engineers and professionals seeking expertise in automated driving systems.
- Software developers interested in autonomous vehicle algorithm development.
- Researchers and academics looking to enhance their knowledge of MATLAB and Simulink for autonomous driving.
- Project managers overseeing automated driving system projects.
- Anyone interested in a career in the autonomous vehicle industry.
- Professionals aiming to stay updated with the latest developments in the field.
Course Outline:
Introduction to Automated Driving Systems
- Understanding the principles of automated driving
- Components and architecture of automated vehicles
- Industry trends and challenges in autonomous driving
- Regulatory and safety considerations
- Case studies of automated driving projects
- Ethical and societal implications of autonomous vehicles
MATLAB Fundamentals for Automated Driving
- Introduction to MATLAB for algorithm development
- Basic scripting and programming in MATLAB
- Data manipulation and visualization
- MATLAB toolboxes for autonomous driving applications
- Debugging and testing techniques in MATLAB
- Best practices for MATLAB coding
Simulink Basics for Automated Driving
- Introduction to Simulink and its role in autonomous systems
- Building and simulating models in Simulink
- Working with blocks and subsystems
- Signal processing and control design in Simulink
- Integrating Simulink with MATLAB for seamless workflows
- Model optimization and performance tuning in Simulink
Sensor Fusion and Perception
- Sensor types and their applications in autonomous vehicles
- Sensor data processing and filtering techniques
- Object detection and tracking algorithms
- Sensor fusion for robust perception
- Simulating sensor data in Simulink
- Case studies on perception systems
Control Systems for Autonomous Vehicles
- Fundamentals of control systems in autonomous driving
- PID and model predictive control (MPC) algorithms
- Path planning and trajectory generation
- Implementing control systems in Simulink
- Real-time control and hardware-in-the-loop (HIL) simulation
- Case studies on control algorithms
Advanced MATLAB and Simulink Features
- Parallel computing and GPU acceleration in MATLAB
- Model-based design and code generation in Simulink
- Integrating AI and machine learning into autonomous systems
- Simulating complex scenarios and environments
- Simulation validation and testing strategies
- Industry applications and future developments