Design, Simulate, and Test Automated Driving Systems with MATLAB and Simulink Training by Tonex
Design, simulate, and test automated driving systems with MATLAB and Simulink is comprehensive training course that equips participants with the knowledge and skills needed to excel in the dynamic field of autonomous vehicle development. Through exercises and real-world examples, attendees will gain a deep understanding of how to leverage MATLAB and Simulink to design, simulate, and test cutting-edge automated driving systems.
This comprehensive training program empowers participants to navigate the complexities of designing, simulating, and testing automated driving systems using MATLAB and Simulink, making them valuable assets in the rapidly evolving field of autonomous vehicles.
Learning Objectives: Upon completion of this course, participants will be able to:
- Master the fundamentals of automated driving systems.
- Develop and customize simulation environments using MATLAB and Simulink.
- Design robust control algorithms for autonomous vehicles.
- Implement sensor fusion techniques for perception.
- Perform comprehensive testing and validation of automated driving systems.
- Stay current with industry best practices and emerging technologies in autonomous vehicle development.
Target Audience: This course is ideal for professionals and enthusiasts in the automotive and engineering industries, including:
- Automotive engineers and researchers.
- Systems and control engineers.
- Robotics engineers.
- Software developers in the automotive sector.
- Graduate students aspiring to work in autonomous vehicle technology.
- Anyone seeking to enhance their skills in MATLAB and Simulink for autonomous systems.
Course Outline:
Introduction to Automated Driving Systems
- Overview of automated driving systems
- Key components and sensors in autonomous vehicles
- Industry trends and challenges
- Safety and regulatory considerations
- Case studies of autonomous vehicle projects
MATLAB and Simulink Fundamentals
- Introduction to MATLAB and Simulink
- Basics of coding in MATLAB
- Simulink modeling and simulation
- Data visualization and analysis in MATLAB
- Integration with other engineering tools
Simulation Environment Development
- Creating realistic environments for testing
- Scenario generation and customization
- Incorporating sensor models
- Handling dynamic objects and traffic
- Real-time simulation considerations
Control Algorithm Design
- Control system architecture for autonomous vehicles
- Path planning and trajectory generation
- Implementing controllers for vehicle dynamics
- Adaptive and machine learning-based control
- Case studies on control algorithm development
Sensor Fusion and Perception
- Sensor types and data fusion
- Object detection and tracking
- Sensor calibration and synchronization
- Environmental perception algorithms
- Handling sensor uncertainties and limitations
Testing and Validation
- Test case development and execution
- Validation methodologies
- Fault detection and safety testing
- Hardware-in-the-loop (HIL) testing
- Compliance with industry standards