Course level:Expert

Certification Course in Advanced Driver Assistance Systems (ADAS) for EVs

The Certification Program in ADAS for Electric Vehicles combines theoretical insights and hands-on training to provide a comprehensive understanding of designing, developing, and simulating Advanced Driver Assistance Systems (ADAS) using MATLAB. With a focus on key sensors, algorithms, and simulation tools, this program equips participants with the skills to create safe, efficient, and scalable ADAS solutions tailored for electric vehicles.

At a glance

  • -10 Modules
  • -36 Lessons
  • -25 Hours of Video Content
  • -Certificate of Completion
  • -1 Project Assignment
$2,000.00
  • Level
    Expert
  • Duration
    25 hours
  • Enrollment validity
    Enrollment validity: Lifetime
  • Certificate
    Certificate of completion

Course Prerequisite(s)

Course Curriculum

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

Module : ADAS Fundamentals & Prerequisites

Module :

Module 3: Introduction to ADAS and MATLAB
This module introduces participants to ADAS, exploring its definition, components, history, and significance in modern automotive systems. Participants gain an overview of MATLAB, including its key features and relevant toolboxes such as Signal Processing, Image Processing, and Automated Driving. The module highlights the benefits of simulation and model-based design, concluding with an activity to explore the MATLAB environment and its basic functions.

Module 4: MATLAB Basics
Participants learn the fundamentals of MATLAB, including syntax, variables, arrays, matrices, and basic operations. The module covers writing scripts and functions, as well as implementing loops and control structures. An activity involving the solution of linear equations using MATLAB scripts and functions helps reinforce these concepts.

Module 5: Data Analysis and Visualization
This module focuses on importing, exporting, and preprocessing data in MATLAB. Participants learn basic statistical analysis and filtering techniques and explore methods for plotting and visualizing data, including 3D plots. The module culminates in an activity analyzing and visualizing ADAS sensor data.

Module 6: ADAS Sensors Overview
Participants are introduced to the working principles, advantages, limitations, and data types of key ADAS sensors such as cameras, RADAR, LIDAR, and ultrasonic sensors. Topics include sensor data formats, preprocessing, and an introduction to sensor fusion. The module concludes with an activity simulating and visualizing sensor data in MATLAB.

Module 7: Signal Processing for ADAS
This module covers digital signal processing basics, including sampling, Fourier transforms, and filtering. Participants learn noise reduction techniques and feature extraction from sensor data, applying these skills in an activity to implement noise reduction filters using MATLAB.

Module 8: ADAS Algorithms
Participants explore algorithms for lane detection, object detection, and tracking, implementing these ADAS features using MATLAB's image processing and computer vision techniques. An activity focuses on developing a lane detection algorithm in MATLAB.

Module 9: Simulating ADAS Systems
This module teaches participants to set up ADAS simulations in MATLAB, including environment and scenario configuration. Using MATLAB’s ADAS Toolbox, participants run simulations and evaluate their performance. The module includes an activity to execute a complete ADAS simulation scenario.

Module 10: Case Study and Project Introduction
This final module reviews key concepts and introduces a comprehensive ADAS project. Participants learn project planning, task division, and timeline creation, preparing to design and simulate a Lane Departure Warning System ADAS as their project.

DIY Projects:

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Hardware & Software Required

Associated Skills

ADAS Automated Driving Toolbox Autonomous Driving Data Analysis Electric Vehicles Image Processing Lane Departure Warning System Lane Detection Algorithm MATLAB RADAR and LIDAR Sensors Real-Time Simulation Sensor Data Preprocessing Sensor Fusion Signal Processing Vehicle Safety Systems

DIY Projects Included

Course Benefits

Technical expertise you will gain

Who can take this course?

Personalized Trainer Support Portal:

At a glance

  • -10 Modules
  • -36 Lessons
  • -25 Hours of Video Content
  • -Certificate of Completion
  • -1 Project Assignment
$2,000.00
  • Level
    Expert
  • Duration
    25 hours
  • Enrollment validity
    Enrollment validity: Lifetime
  • Certificate
    Certificate of completion

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