Projects
Assignments
Overview
This “Certified Automotive Embedded Software Engineer” program is a comprehensive training initiative designed to equip aspiring and practicing engineers with the essential skills and knowledge required to excel in the rapidly evolving automotive industry. The program focuses on building a strong foundation in software development, embedded systems, and automotive safety standards, enabling participants to contribute effectively to the development of cutting-edge automotive technologies.
The curriculum is structured to provide a logical progression, starting with fundamental programming concepts and advancing to specialized automotive engineering topics. Through a blend of theoretical instruction, practical exercises, and real-world case studies, participants will gain hands-on experience and develop the expertise needed to design, develop, and validate automotive software and systems.
Objectives
Upon successful completion of this program, participants will be able to:
- Master Fundamental and Advanced Programming Skills
- Comprehend and Apply Automotive Safety Standards
- Utilize Model-Based Development (MBD) Techniques
- Develop Expertise in In-Vehicle Networking and Embedded Systems
- Perform Rigorous Software Verification and Validation
- Contribute Effectively to Automotive Software Development Projects
Prerequisites
All participants should have:
- C Programming Foundations, Micocontroller/Microprocessor Fundamentals
- Basic Control System and Digital Signal Processing will be helpful
Suggested Audience
Entry level Embedded Engineers/Programmers, Final Year Engineering students aspiring to be automotive embedded engineers, Fresh Corporate Engineers (Planned to be Deployed in Automotive Embedded Development/Test projects)
Duration
240+ Hours Approx – (6 Courses * 40 hours each)
Course Outline
- Module 1: Modern C++ Fundamentals and Resource Management
- Module 2: Template Metaprogramming (TMP)
- Module 3: C++ Standard Template Library (STL) for Embedded
- Module 4: Introduction to Python
- Module 5: Data Structures
- Module 6: Functions and Modules
- Module 7: Mathematical Operations for Autonomous Driving
- Module 1: Introduction to Deep Learning for Autonomous Driving
- Module 2: Artificial Neural Networks (ANN)
- Module 3: Convolutional Neural Networks (CNN)
- Module 4: OpenCV for Computer Vision
- Module 5: YOLO for Object Detection
- Module 6: Deployment and Optimization
- Module 1: Introduction to Perception for Autonomous Driving
- Module 2: Lane Detection
- Module 3: Scene Detection and Understanding
- Module 4: Static and Dynamic Object Detection
- Module 5: Camera and Lidar Systems
- Module 6: ROS-CARLA Bridge
- Module 7: Perception Simulation in CARLA
- Module 1: Introduction to Localization and Coordinate Systems
- Module 2: State Estimation Techniques
- Module 3: Kalman Filter (KF)
- Module 4: Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF)
- Module 5: Lidar Localization
- Module 6: Particle Filter
- Module 1: Introduction to Motion Planning
- Module 2: Path Planning Techniques
- Module 3: Motion Planning with Potential Fields
- Module 4: Optimization-Based Motion Planning
- Module 5: Rapidly-exploring Random Trees (RRT and RRT)*
- Module 6: Motion Planning for Autonomous Driving
- Module 1: Introduction to Motion Control
- Module 2: Basic Control Techniques
- Module 3: Optimization Frameworks (CasADi)
- Module 4: Model Predictive Control (MPC)
- Module 5: Vehicle Dynamics and Control
- Module 6: Implementation in CARLA-ROS
