Why this course?

Description

This comprehensive training program is designed to provide an in-depth understanding of autonomous vehicle technology and its application. It covers key aspects such as sensor integration, object detection, motion planning, and scenario generation using MATLAB Simulink. The course begins with a broad introduction to autonomous vehicles, before diving into practical tasks such as building driving scenarios, parking maneuvers, and pedestrian tracking.

Overview:

This course on Autonomous Vehicles is structured to provide you with a comprehensive understanding of how autonomous vehicles work and how to design, simulate, and optimize their operation using MATLAB Simulink. Starting with the basics of autonomous vehicle systems, the course covers key topics like sensors, detection, object tracking, scenario creation, and motion planning. You'll gain practical experience by working with real-world data and simulating scenarios in a safe, virtual environment. Additionally, it explores how to create and implement autonomous vehicle systems using Simulink and other tools

• Tools:

  • MATLAB Simulink
  • Ground Truth Labeler App
  • Driving Scenario Designer App
  • Dynamic Occupancy Grid Map
  • Simulink Code Generation Tools
    
    Skills Required:
    • Basic understanding of MATLAB
    • Familiarity with Simulink
    • Knowledge of object detection and tracking techniques
    • Understanding of Model-Based Design (MBD)

Basic knowledge of motion planning and vehicle control

Projects:

Design and simulate a driving scenario for autonomous vehicles.

Create and implement a parking maneuver scenario.

Develop motion planning systems for urban environments using dynamic grid maps.

Simulate and track pedestrians in real-time video sequences.
 

Tools:

 

MATLAB

A key tool for mathematical modeling, simulation, and algorithm development.

Simulink

An extension of MATLAB used for modeling, simulating, and analyzing multidomain dynamical systems, particularly useful for designing autonomous vehicle systems.

Driving Scenario Designer App (MATLAB)

Used for creating, simulating, and visualizing driving scenarios for autonomous vehicle systems.

Ground Truth Labeler App (MATLAB)

Tool for manually labeling data (e.g., identifying vehicles, pedestrians) in video sequences, which is important for training object detection algorithms.

Dynamic Occupancy Grid Map (MATLAB)

A tool used for motion planning and navigation in autonomous vehicles, providing spatial data for decision-making.

Stateflow (Simulink)

A tool for designing and simulating state machines and control logic, particularly useful for modeling decision-making systems in autonomous vehicles.

Code Generation Tools

MATLAB and Simulink tools used to automatically generate code from models, enabling rapid deployment to hardware.
 

Topics Covered by Day:

 

• Day 1: Introduction to Autonomous Vehicle
• Day 2: Sensors in Automated Vehicle
• Day 3: Sensor Coverage, Detection, and Tracks in MATLAB Simulink
• Day 4: Get Started with Ground Truth Labeler App in MATLAB Simulink
• Day 5: Load Ground Truth Signal in the App and Label the Car in Groundtruth Labeler App
• Day 6: Detect the Car in AV Video Sequence Using GMM in MATLAB Simulink
• Day 7: Multiple Object Tracking on the Road (Video Sequence) in MATLAB Simulink
• Day 8: Create Scenario Using Driving Scenario Designer App in MATLAB Simulink
• Day 9: Creating Driving Scenario Using Designer
• Day 10: Creating Driving Scenario Programmatically
• Day 11: Creating Driving Scenario Programmatically - II
• Day 12: Import ASAM OpenDrive Road File to Driving Scenario Designer App
• Day 13: Define Road Layout Programmatically in MATLAB Simulink
• Day 14: Scenario Generation from Recorded Data
• Day 15: Scenario Generation from Recorded Data - Continued
• Day 16: Simulate Vehicle Parking Maneuver in Driving Scenario App Using MATLAB Simulink
• Day 17: Simulate Vehicle Parking Maneuver - Continued
• Day 18: Create Reverse Motion Driving Scenarios Interactively
• Day 19: Tracking Pedestrians from a Moving Car
• Day 20: Motion Planning in Urban Environments Using Dynamic Occupancy Grid Map
• Day 21: Motion Planning in Urban Environments Using Dynamic Occupancy Grid Map - Continued
• Day 22: MBD Introduction (Model-Based Design)
• Day 23: Code Generation for Simulink Model
• Day 25: Turning and Braking at the Intersection, Moving Vehicle, and Reversing the Vehicle in Scenario
• Day 26: Simulink Blocks Bus Creator and Selector and MUX and Demux Uses
• Day 27: Design Up Counter and Down Counter Using Simulink Blocks
• Day 28: Stateflow Introduction and State Action Types
• Day 29: Additional Scenarios and Advanced Techniques
• Day 30: Final Integration and Review