Course Syllabus:

Module 1: Electric Vehicle Technology I

• • Topic 1: Starting with EV Technology
  • Topic 2: Understanding ICE to EV Transition
  • Topic 3: Electric Vehicle Engineering
  • Topic 4: Battery Technology for EV Systems

Module 2: Electric Vehicle Technology II

• • Topic 1: Power Electronics for EV Systems
  • Topic 2: Motor Systems for Electric Vehicles
  • Topic 3: Vehicle Electrification Systems
  • Topic 4: Electric Vehicle Charging Technology

Module 3: Introduction to ADAS & MATLAB

• • Topic 1: Overview of ADAS – Definition, Components, History, and Significance.
  • Topic 2: MATLAB overview – Key features, relevant toolboxes (Signal Processing, Image Processing, Automated Driving
  • Topic 3: Benefits of Simulation & Model Based Design
  • Topic 4: Activity – Explore MATLAB Environment & Basic Functions

Module 4: MATLAB Basics

• • Topic 1: MATLAB Syntax, Operations, Variables, Arrays, & Matrices
  • Topic 2: Writing Scripts and Functions, including Loops & Control Structures
  • Topic 3: Activity – Solve Linear Equations using MATLAB scripts & functions

Module 5: Data Analytics & Visualization

• • Topic 1: Importing, Exporting, & Pre-Processing Data in MATLAB
  • Topic 2: Basic Statistical Analysis & Filtering Techniques
  • Topic 3: Plotting & Visualizing Data, including 3D plots
  • Topic 4: Activity – Analyze and Visualize ADAS Sensor Data

Module 6: ADAS Sensor Overview

• • Topic 1: Overview of Camera, RADAR, LIDAR, and Ultrasonic Sensors – Working Principles, Advantages, Limitations, & Data Types
  • Topic 2: Sensor Data Formats & Pre Processing
  • Topic 3: Introduction to Sensor Fusion
  • Topic 4: Activity – Simulate and Visualize Sensor Data in MATLAB.

Module 7: Signal Processing for ADAS

• • Topic 1: Basics of Digital Signal Processing: Sampling, Fourier Transforms, & Filtering
  • Topic 2: Noise Reduction & Feature Extraction from Sensor Data
  • Topic 3: Activity – Implement Noise Reduction Filters on Sensor Data using MATLAB

Module 8: ADAS Algorithm

• • Topic 1: Algorithms for Lane Detection, Object Detection, and Tracking
  • Topic 2: Implementing ADAS Algorithms in MATLAB using Image Processing & Computer Vision Techniques
  • Topic 3: Activity – Develop a Lane Detection Algorithm using MATLAB

Module 9: Simulating ADAS Systems

• • Topic 1: Setting up ADAS Simulations in MATLAB – Environment & Scenario Setup
  • Topic 2: Using MATLAB’s ADAS Toolbox for Simulation
  • Topic 3: Evaluating Simulation Performance
  • Topic 4: Activity – Run an ADAS Simulation Scenario & Evaluate Its Performance

Module 10: Case Study & Project introduction 

• • Topic 1: Review of Key Concepts & Introduction to a Comprehensive ADAS Project 
  • Topic 2: Project Planning, Task Division, and Timeline Creation
  • Topic 3: Activity – Begin Project Planning & Role Assignments
    
    

Who Should Enroll:

This course is tailored for electrical, automotive, and software engineers, as well as professionals in the automotive industry looking to deepen their knowledge of ADAS technologies and their implementation.

Learning Outcomes:

• Develop comprehensive knowledge of ADAS technologies and their applications.
• Gain proficiency in MATLAB for simulating and analyzing ADAS systems.
• Acquire hands-on experience in integrating and programming embedded systems for ADAS.
• Prepare for advanced roles in automotive engineering and related fields.

Projects and Practical Experience:

Participants will complete several hands-on projects, including:

• ADAS feature simulation using MATLAB
• Sensor and actuator interfacing projects
• Real-world case study analysis and project implementation