- Program Highlights
The Advanced Modeling, ADAS & AUTOSAR Program for Automotive & EV Systems is a comprehensive 24-week, LIVE + Self-Paced career-focused program built to prepare engineers for the future of intelligent mobility and autonomous systems. The program offers hands-on, end-to-end training in both ADAS (Advanced Driver Assistance Systems) and AUTOSAR (Automotive Open System Architecture) — with simulation and modeling expertise. Learners gain practical experience in automotive software design, simulation, and validation using industry-standard tools such as MATLAB, Simulink, QSS, Python, and machine learning frameworks. With in-depth modules on EV system design, sensor fusion, safety systems, and embedded software architecture, participants develop the technical expertise required to thrive in careers across electric vehicles, autonomous driving, and automotive software engineering.
- Admission Closes on 1st Nov
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- Career Opportunities
- Advanced Driver Assistance Systems (ADAS) Development
- AUTOSAR-based Embedded Software Development
- Model-Based Development (MBD) for Automotive Applications
- EV Powertrain & Battery Management Systems (BMS) Software Engineering
- Functional Safety (ISO 26262) & Cybersecurity (ISO 21434) Compliance
- Vehicle Communication (CAN, LIN, FlexRay, Ethernet) Engineering
- AI & Computer Vision for Autonomous Vehicles
- Real-Time Operating Systems (RTOS) & Multi-Core Processing
- HIL/SIL/MIL Testing for ADAS & EV Systems
- ADAS Software Engineer (Radar, LiDAR, Camera Sensor Fusion)
- AUTOSAR Embedded Software Engineer (Classic & Adaptive AUTOSAR)
- Model-Based Development Engineer (MATLAB, Simulink, Embedded C)
- Vehicle Communication Engineer (CAN, LIN, FlexRay, SOME/IP)
- Functional Safety Engineer (ISO 26262 Compliance)
- Cybersecurity Engineer (ISO 21434 & Secure Boot Implementation)
- Battery Management System (BMS) Engineer (EV Powertrain & Battery Control)
- HIL Validation Engineer (SIL, MIL, HIL Testing using dSPACE, CANoe)
- AI & Perception Engineer (Computer Vision & Sensor Fusion for ADAS)
- AUTOSAR Classic & Adaptive (Architecture, RTE, MCAL, BSW, OS, COM)
- MATLAB/Simulink & Model-Based Development (MIL, SIL, HIL)
- AI & Machine Learning for ADAS (Python, OpenCV, TensorFlow)
- Vehicle Communication Protocols (CAN, LIN, FlexRay, SOME/IP, DDS)
- Embedded C & C++ for Automotive Software Development
- ADAS Sensor Integration (Radar, LiDAR, Camera, Ultrasonic Sensors)
- ISO 26262 Functional Safety & ISO 21434 Cybersecurity Implementation
- BMS, Power Electronics & Motor Control Strategies for EVs
- HIL Testing & Validation (Vector CANoe, dSPACE, NI LabVIEW)
- Real-Time Operating Systems (RTOS) for Automotive ECUs
- Tata Motors
- Mahindra Electric Mobility Limited
- Hero Electric
- Ather Energy
- Ola Electric
- TVS Motor Company
- Bajaj Auto
- MG Motor India
- Hyundai Motor India
- Ashok Leyland
- JBM Auto
- Kinetic Green Energy & Power Solutions
- FOR ENTERPRISE
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- Program Outcomes
- Program Curriculum
Module 1: EV Engineering Essentials Part I and II
- Module Description:
- In this foundational module, you’ll explore the essential components and technologies of electric vehicles, covering the transition from internal combustion engines (ICE) to EVs. This part covers EV design principles, key subsystems, and safety considerations, along with battery technologies, power electronics (inverters, converters), and motor systems, with a focus on control strategies and efficiency optimization. You’ll also gain insights into vehicle electrification, voltage distribution, and the latest charging technologies, including standards and fast-charging solutions.
- Module Details:
- Starting with EV Technology: Overview of Electric Vehicles, Components, Key Technologies.
- Understanding ICE to EV Transition: Key Differences, Benefits, Challenges.
- Electric Vehicle Engineering: EV Design Principles, Key Subsystems, Safety Considerations.
- Battery Technology for EV Systems: Battery Chemistries, Energy Density, Charging Cycles
- Power Electronics for EV Systems: Inverters, Converters, Power Management.
- Motor Systems for Electric Vehicles: Types of Motors, Control Strategies, Efficiency Optimization.
- Vehicle Electrification Systems: Vehicle Wiring, Voltage Distribution, High and Low Voltage Components.
- Electric Vehicle Charging Technology: Charging Infrastructure, Standards (CCS, CHAdeMO), Fast Charging Solutions.
Module 2: Advanced Certification in Electric Vehicle Design and Simulation using MATLAB, SIMULINK, and QSS
- Module Description:
- This module covers key aspects of electric vehicle (EV) development, including architecture modeling, powertrain design, and energy flow simulations. It analyzes road load factors like aerodynamic drag, rolling resistance, and gradient forces. The module also focuses on inverter design, efficiency, and thermal management, along with advanced Simscape modeling for electrical, mechanical, and thermal systems. Additionally, it integrates QSS and ADVISOR toolboxes for vehicle design, energy efficiency analysis, and battery performance modeling.
- Module Details:
- Road Load Understanding: Forces Acting on Vehicles, Load Distribution
- Road Load Analysis: Modeling Aerodynamic Drag, Rolling Resistance, Gradient Forces
- Inverter Design and Modeling: Sizing, Efficiency, Thermal Management
- Advanced Simscape Modeling: Electrical, Mechanical, Thermal Modeling for EV Systems
- QSS and ADVISOR Toolbox Applications: Vehicle Design Simulations, Energy Efficiency Analysis
- BMS Modeling and Energy Analysis: Battery Performance Modeling, Energy Flow Simulations
Module 3: Introduction To AUTOSAR
- Module Description:
- This module introduces AUTOSAR, covering its definition, history, and key goals. It highlights the importance of AUTOSAR for standardization, scalability, and reusability in automotive applications.
- Module Details:
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Course Overview & Learning Outcomes
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Guidelines for Course Participation
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Introduction to AUTOSAR – Definition, History, and Goals
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Why AUTOSAR is Essential – Standardization Benefits, Scalability & Reusability in Automotive Applications.
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Module 4: Basic Software (BSW) Layer
- Module Description:
- This module provides an overview of the AUTOSAR Basic Software architecture, focusing on key layers such as the Services Layer, ECU Abstraction Layer, and Microcontroller Abstraction Layer (MCAL). Learners explore how standardized interfaces and the communication stack enable hardware-independent software development in automotive systems.
- Module Details:
- Overview of the AUTOSAR Basic Software (BSW)
- Introduction to BSW Module
- Services Layer
- ECU Abstraction Layer
- Microcontroller Abstraction Layer (MCAL)
- AUTOSAR Layered Architecture:
- Communication Stack
- Interface Abstractions & Standardized APIs
Module 5: Software Components (SWC)
- Module Description:
- This module covers the roles of Atomic and Composite Software Components in AUTOSAR. It introduces component composition through functional grouping and reusability patterns, enabling modular and scalable automotive software design..
- Module Details:
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Software Components and Their Roles:
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Atomic Software Components
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Composite Software Components
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- Composition Examples:
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Functional Grouping & Reusability Patterns
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Module 6: Ports & Interfaces
- Module Description:
- This module focuses on the communication mechanisms between AUTOSAR Software Components using well-defined ports and interfaces. Learners explore the Sender-Receiver (SR) and Client-Server (CS) models, which enable data exchange and service-oriented communication. The module also covers port-level interface definitions, connectors, and port compositions, with practical examples demonstrating how components interact within an automotive ECU environment.
- Module Details:
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Port-Level Interface Definitions in AUTOSAR
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Sender-Receiver (SR) Communication Model – Data Exchange Principles
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Client-Server (CS) Interface – Service-Oriented Communication
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Connectors & Port Compositions
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Practical Examples – Port-to-Port Communication Scenarios
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Module 7: Runnables And Events
- Module Description:
- This module focuses on Runnables in AUTOSAR, which serve as the execution entities within Software Components (SWCs). It details the various types of events that trigger these Runnables to execute, including Time Events that occur at specific intervals, Mode Switch Events triggered by changes in system modes, and Operation Invocations initiated by function calls.
- Module Details:
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Runnables in AUTOSAR – Execution Entities of SWCs.
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Events Triggering Execution:
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Time Events
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Mode Switch Events
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Operation Invocations
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Module 8: Application Software Component (ASW)
- Module Description:
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This module provides an overview of the Application Software (ASW) Layer, focusing on the design of its functional layer. It covers how various functionalities are mapped and organized into Software Components (SWCs), enabling modular and efficient software development in AUTOSAR systems.
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- Module Details:
- ASW Layer Overview – Functional Layer Design
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Module 9: Run-Time Environment (RTE)
- Module Description:
- This module introduces the Run-Time Environment (RTE) layer, which acts as middleware between Software Components (SWCs) and the Basic Software (BSW) layer. It covers the two main RTE communication models: Sender-Receiver and Client-Server. The module explains the end-to-end communication flow facilitated by the RTE, along with event scheduling and triggering mechanisms managed through the RTE. Additionally, it includes the process of RTE code generation using AUTOSAR builder tools and the output artifacts produced.
- Module Details:
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Introduction to RTE Layer – Roles.
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Middleware between SWC and BSW
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Sender-Receiver RTE Communication
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Client-Server RTE Communication
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End-to-End Communication Flow using RTE
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Event Scheduling and Triggering via RTE
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RTE Code Generation – AUTOSAR Builder Tools & Output Artifacts.
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Module 10: AUTOSAR Development Methodology
- Module Description:
- This module covers the AUTOSAR development methodology, focusing on the engineering approaches used in AUTOSAR including top-down and bottom-up development. It explains the complete workflow of system design, configuration, and integration within AUTOSAR, supplemented by real-world examples illustrating the lifecycle of component development.
- Module Details:
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Engineering Approach in AUTOSAR – Top-down vs. Bottom-up Development.
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AUTOSAR Methodology Workflow – System Design, Configuration, Integration.
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Real-World Examples – Lifecycle of Component Development
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Module 11: Introduction To ADAS
- Module Description:
- This module introduces the fundamental concepts of Advanced Driver Assistance Systems (ADAS), explaining their essential role in modern vehicles. It provides a visual understanding of ADAS components and how they interact within the system. Additionally, the module explores the importance of ADAS as the foundational technology for autonomous driving and covers key terminology and global standards related to ADAS.
- Module Details:
- What is ADAS – Understand the core concept of Advanced Driver Assistance Systems and their role in modern vehicles.
- Block Diagram Representation – Visual overview of ADAS components and system interactions.
- Role of ADAS in Today’s Autonomous Driving – Explore how ADAS is building block for autonomous vehicle technologies.
- Specific Terms and Standard Definitions – Learn common terminology and global standards relevant to ADAS.
Module 12: Implementation Of ADAS in MATLAB
- Module Description:
- This module focuses on practical implementation of Advanced Driver Assistance Systems (ADAS) using MATLAB. It covers simulation and modeling of automotive radar systems for object detection. The camera-based vision system section deals with image acquisition and object recognition through computer vision techniques. You will also learn to simulate ultrasonic sensors for close-range object detection and parking assistance. The module includes LIDAR technology for 3D mapping and obstacle detection using laser-based systems. Finally, it addresses integration of GNSS, GPS, and IMU sensors to support accurate vehicle positioning and navigation..
- Module Details:
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Automotive Radar – Simulation and modeling of radar-based object detection.
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Camera (Vision System) – Image acquisition and object recognition using computer vision.
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Ultrasonic Sensor – Close-range object detection and parking assistance simulation.
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LIDAR – 3D mapping and obstacle detection using laser-based systems.
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GNSS, GPS, IMU – Integration of location and motion sensors for vehicle positioning and navigation.
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Module 13: Machine Learning Implementation In Adas
- Module Description:
- This module introduces the use of AI, ML, and DL in ADAS, focusing on how data-driven models enhance driving safety. It covers core concepts like classification, detection, and prediction, along with sensor fusion for improved accuracy and the hardware requirements for real-time AI processing in vehicles.
- Module Details:
- Introduction to Machine Learning in ADAS – Role of Al and data-driven models in enhancing driving safety
- ML & DL in ADAS – Overview – Introduction to classification, detection, and prediction using ML/DL
- ML & DL in ADAS – Sensors and Sensor Fusion – Combining data from multiple sensors for improved accuracy
- ML & DL in ADAS – Processors – Understanding hardware requirements and optimization for Al models.
Module 14: Safety Systems In ADAS– Part 1
- Module Description:
- This course offers a compact overview of ADAS safety features that help prevent or reduce collisions. Topics include Adaptive Cruise Control, Cross Traffic Alerts, Blind Spot Detection, Lane Change Assist, Parking & Headlight Assistance, as well as Occupant, Pedestrian, and Evasive Protection Systems — all designed to enhance situational awareness and driving safety.
- Module Details:
Introduction – Overview of safety features that prevent or reduce collision risks
Adaptive Cruise Control (ACC) – Maintaining safe distance and speed relative to surrounding vehicles
Rear Cross Traffic Alert (RCTA) – Detecting approaching vehicles while reversing
Vehicle Exit Alert – Warning when opening doors into oncoming traffic
Front Cross Traffic Alert – Detecting vehicles approaching from the side at junctions
Forward Collision Warning – Alerting drivers of imminent front-end collisions
Vehicle Turning Assistance – Monitoring blind spots and surrounding vehicles during turns
Blind Spot Detection – Detecting vehicles in the driver’s blind zone
Lane Change Assist – Supporting safe and timely lane changes
Parking Assistance System – Automated steering and alerts during parking
Intelligent Headlight Control – Adjusting beam intensity and direction based on traffic conditions
Occupant Protection System – Pre-crash systems like airbag deployment and seatbelt tensioning
Pedestrian Protection System – Sensing and mitigating risk to pedestrians
Evasive Steering Support – Assisting in controlled steering during obstacle avoidance
Module 15: Safety System In ADAS- Part 2
- Module Description:
This module explores advanced ADAS technologies that enhance driver awareness and vehicle stability. It covers systems like Traffic Sign Recognition, Speed Limit Assist, Lane Departure Warning, and 360° View Cameras, alongside critical safety features such as Driver Monitoring, Emergency Brake Assist, ABS, TCS, and Cross Wind Assist—all designed to improve response, control, and road safety.
- Module Details:
Introduction – Advanced systems for environment awareness and driver support
Traffic Sign Recognition System – Detecting and interpreting road signs using vision-based systems
Speed Limit Assist – Monitoring and maintaining legal speed limits dynamically
Lane Departure Warning – Alerting drivers when unintentionally drifting out of lanes
360° Surrounding View System – Providing a bird’s-eye view for enhanced spatial awareness
Driver Monitoring System – Observing driver behavior for signs of distraction or fatigue
Driver Drowsiness Detection – Identifying early signs of driver fatigue
Emergency Brake Assist – Supporting braking when sudden obstacles are detected
Anti-lock Braking System (ABS) – Preventing wheel lock during hard braking
Traction Control System (TCS) – Maintaining vehicle stability on slippery surfaces
Cross Wind Assist – Stabilizing vehicle in strong lateral wind conditions
Module 16: Testing And Validation In ADAS
- Module Description:
- Learn the validation of safety-critical ADAS features through rigorous testing techniques. Covers functional, scenario-based, and performance testing, including Simulation, SIL, HIL, and DIL methods. Also explores on-track and real-world validation approaches essential for ensuring ADAS reliability and road readiness..
- Module Details:
- Introduction – The critical need for rigorous validation in safety-critical ADAS applications
- Overview of ADAS Testing – Functional, scenario-based, and performance testing approaches
- Simulation, SIL, HIL, and DIL Testing – Model-in-the-loop and hardware-in-the-loop validation techniques
- On-Track Testing and Real-World Validation – Physical testing on proving grounds and public roads
- Skills Covered
- Benefits
- Entry-Level Training in ADAS, AUTOSAR & EV Systems for Beginners.
- Industry-Oriented Curriculum to Bridge the College-to-Career Gap.
- Hands-on Projects & Real-World Simulations for Practical Learning.
- Job Readiness for Automotive, EV & Embedded Software Engineering Roles.
- Exposure to Cutting-Edge Automotive Al & Machine Learning Applications.
- Certification & Placement Support for Fresh Graduates in Automotive Industry.
- Upskilling for Experienced Automotive Engineers transitioning into ADAS & AUTOSAR.
- Bridging the Gap between Embedded Software & EV System Development.
- Certification & Industry Recognition to enhance career growth.
- Gaining Hands-on Experience with Industry-Standard Tools like Vector CANoe, MATLAB, Simulink.
- Understanding Functional Safety &
- Cybersecurity Compliance for regulatory adherence.
- Advanced Knowledge of Al in ADAS for future autonomous vehicle roles.
- Ability to design AUTOSAR-based software architectures for EVs and ADAS.
- Hands-on experience with CAN, LIN, and Ethernet-based communication in vehicles.
- Understanding of sensor fusion techniques for Radar, LiDAR, and Camera systems.
- Proficiency in model-based development (MBD) using MATLAB/Simulink.
- Knowledge of ISO 26262 (Functional Safety) and ISO 21434 (Cybersecurity) standards.
- Ability to configure and implement AUTOSAR Classic and Adaptive Platforms.
- Hands-on experience with HIL (Hardware-in-the-Loop) testing for ADAS applications.
- Proficiency in Al-based perception models using OpenCV and TensorFlow.
- Expertise in powertrain control and battery management in electric vehicles.
- Readiness for industry roles in ADAS, AUTOSAR, and EV software engineering.
- Projects
Design an AUTOSAR-based intelligent parking assistant using sensor fusion.
Develop and test ACC algorithms using MATLAB/Simulink and AUTOSAR integration.
Implement LKA with camera-based lane detection and vehicle control strategies.
Model and analyze EV BMS using MATLAB, Simulink, and AUTOSAR.
- Mode of Learning
Complete on-site
classroom program
Location: Mumbai
LIVE + Recorded + Onsite + Hardware + Workshop
LIVE + Weekend on-site sessions
Location: Pune, Delhi
LIVE + Recorded + Hardware + Workshop
Location: Global
- Tools Covered
