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.

Level

Expert
Total Enrolled

40
Duration

25 hours
Enrollment validity

Enrollment validity: Lifetime
Certificate

Certificate of completion

Course Prerequisite(s)

Please note that this course has the following prerequisites which must be completed before it can be accessed
Electric Vehicle Essentials 2: Motors, Electrification and Charging Systems
Electric Vehicle Essentials 1: Industry Ecosystem & Battery Technology

Course Curriculum

Module 1: ADAS Fundamentals & Prerequisites

Topic 1: ADAS Prerequisites 1 – Vehicle Fundamentals & Electronics

12:02
Topic 2: ADAS Prerequisites 2 – Embedded Systems, Programming & Control

13:58
Topic 3: ADAS Prerequisites 3 – AI & ML, Safety Standards & Testing

17:04
Topic 4: ADAS Overview – Sensors, Features & Automation

12:41

Module 2: Comprehensive ADAS Development & Simulation Using MATLAB-Simulink

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:

Add this certificate to your resume to demonstrate your skills & increase your chances of getting noticed.

Student Ratings & Reviews

No Review Yet

Hardware & Software Required

Hardware:

High-performance PC (Intel i7 or higher)
16GB RAM

Software:

MATLAB
Signal Processing Toolbox
Image Processing Toolbox
Automated Driving Toolbox

DIY Projects Included

Project: Lane Departure Warning System ADAS Design and Simulation

Course Benefits

For Professionals:

Master advanced MATLAB features for ADAS development.
Enhance expertise in sensor data analysis and fusion.
Expand career opportunities in autonomous driving and safety systems.
Learn industry-standard ADAS design and simulation practices.
Gain hands-on experience with real-world ADAS projects.

For Freshers:

Build a strong foundation in ADAS and sensor technologies.
Gain practical experience in MATLAB programming and simulation.
Enhance employability in the growing field of autonomous systems.
Develop a comprehensive project for a strong portfolio.
Enter the automotive industry with in-demand skills.

Technical expertise you will gain

Design and Simulate ADAS features using MATLAB.
Analyze sensor data & implement sensor fusion techniques.
Develop algorithms for lane detection, object detection, and tracking.
Set up and evaluate ADAS simulations in MATLAB.
Use MATLAB toolboxes for signal and image processing.
Integrate and test sensor data in a simulated environment.
Design and simulate a Lane Departure Warning System.

Key Job Areas

Job Roles

Skill Sets

Companies Hiring

Key Job Areas

Job Roles

Skill Sets

Companies Hiring

Who can take this course?

The course is suitable for those students who have:

Basic understanding of vehicle dynamics and automotive systems.
Familiarity with programming concepts and MATLAB basics (optional but recommended).
Interest in ADAS, simulation, and sensor technologies.

Personalized Trainer Support Portal:

24/7 Access to a personalized trainer support portal.
One-on-One Mentorship for queries and project guidance.
Access to diverse resources, including recorded lectures, reading materials, and practical guides.
Dedicated forums for content discussion, insights, and project collaboration.
Regular Feedback from trainers for comprehensive understanding and improvement

Level

Expert
Total Enrolled

40
Duration

25 hours
Enrollment validity

Enrollment validity: Lifetime
Certificate

Certificate of completion

★ Rated 4.8/5 by 5,345+ verified learners | 50,000+ professionals trained across 170+ countries

★★★★★

4.8/5 5,345 Verified Reviews

50,000+ Alumni Trained

300+ Hiring Partners

₹8.4 LPA Average Package

170+ Countries Reached

Trust & Recognition

Government-Certified. Industry-Endorsed. Learner-Approved.

India's largest ASDC-certified EV training organisation — with multi-layer Government of India recognitions, 300+ industry hiring partners, and a 4.8-star verified learner rating.

ASDC Certified

Automotive Skills Development Council, Government of India — recognised by every major auto OEM.

Verified Credential

AICTE NEAT

National Educational Alliance for Technology — AICTE's premium learning ecosystem.

Verified Credential

Ministry of Education

Recognised by Ministry of Education, Government of India, and NITI Aayog.

Verified Credential

Our 50,000+ Alumni Work At

Top OEMs, Tier-1 suppliers, and EV startups hire DIYguru-certified professionals

Placement powered by emobility.careers — India's only EV-specific job portal

Featured In & Endorsed By

The Economic TimesFeatured

YourStoryFeatured

ET AutoFeatured

Autocar IndiaFeatured

Mobility OutlookFeatured

The Hindu BusinessLineFeatured

Verified Reviews

What Our Alumni Say

Real feedback from verified learners who completed the programme and moved into EV careers.

Verified

★★★★★

"The hands-on workshops and live projects gave me real EV skills. I got placed at Tata Motors within 2 months of certification. The BMS module is especially strong."

Rahul Sharma Battery Engineer · Tata Motors

Verified

★★★★★

"Switched from a mechanical design role to EV powertrain after the course. The curriculum is industry-aligned and mentors are from top EV companies. Totally worth it."

Priya Krishnan Powertrain Engineer · Ola Electric

Verified

★★★★★

"Best EV training in India, hands down. The placement support through emobility.careers was what actually made the difference. Interviewed at 6 companies, got 3 offers."

Arjun Verma BMS Developer · Ather Energy

Ready to Launch Your EV Career?

Get free 1-on-1 counselling from a DIYguru EV career advisor. We'll help you pick the right course, explain placement support, and share the next batch dates.

Free career counselling within 24 hours
Personalised course recommendation
EMI options starting ₹4,083/month at 0%
Direct access to 300+ EV hiring partners

Get Free EV Career Counselling

Speak with an advisor in 24 hours

⭐ 4.8/5 from 5,345+ verified learners

🔒 Your data is 100% secure. Lead is pushed to DIYguru CRM — no third parties.

eMobility Community

Instructor

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

At a glance

Course Prerequisite(s)

Course Curriculum

Module 1: ADAS Fundamentals & Prerequisites

Topic 1: ADAS Prerequisites 1 – Vehicle Fundamentals & Electronics

Topic 2: ADAS Prerequisites 2 – Embedded Systems, Programming & Control

Topic 3: ADAS Prerequisites 3 – AI & ML, Safety Standards & Testing

Topic 4: ADAS Overview – Sensors, Features & Automation

Module 2: Comprehensive ADAS Development & Simulation Using MATLAB-Simulink

Topic 1: Overview of Development Models, Testing, and Simulation

Topic 2: LDW & LKA – Sensor Based Implementation & Validation in MATLAB

Topic 3: MATLAB and Simulink Overview for ADAS Applications

Topic 4: Line-by-Line Code Demonstration – MATLAB Simulation of ADAS Lane Keeping System

Topic 5: Camera-Based Lane Detection and Video Processing for ADAS Using MATLAB

Topic 6: Sensor Limitations and ADAS Functional Block Diagram

Topic 7: Radar-Based Object Detection in ADAS – Principles, MATLAB Simulation, & Code Walkthrough

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

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

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.

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 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.

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

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 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.

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

DIY Projects:

Project: Lane Departure Warning System ADAS Design and Simulation

Congratulations on Successfully Completing the Course!

Earn a certificate

Student Ratings & Reviews

Hardware & Software Required

Hardware:

Software:

Associated Skills

DIY Projects Included

Project: Lane Departure Warning System ADAS Design and Simulation

Course Benefits

For Professionals:

For Freshers:

Technical expertise you will gain

Who can take this course?

Personalized Trainer Support Portal:

At a glance

Government-Certified. Industry-Endorsed. Learner-Approved.

ASDC Certified

AICTE NEAT

Ministry of Education

Our 50,000+ Alumni Work At

What Our Alumni Say

Ready to Launch Your EV Career?

Get Free EV Career Counselling

Thank you!

Let's Connect

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 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.