Online/ e-LMS
Self Paced
Moderate
3 Weeks
About
The program delves into the AI and robotics techniques that empower autonomous vehicles, including computer vision, object detection, sensor fusion from LIDAR and radar, and motion planning algorithms. This hands-on course also covers real-world challenges like obstacle avoidance, lane detection, and decision-making for AVs.
Aim
This program teaches the key technologies and AI methodologies that enable autonomous vehicles (AVs) to perceive, decide, and navigate through complex environments. Participants will gain knowledge of deep learning, sensor fusion, and path planning techniques essential for AV systems.
Program Objectives
- Learn the core AI technologies that enable self-driving vehicles.
- Master sensor fusion, perception, and motion planning for AVs.
- Understand computer vision techniques for road and object detection.
- Implement real-time decision-making with reinforcement learning.
- Gain hands-on experience in building an AI-driven autonomous vehicle system.
Program Structure
- Introduction to Autonomous Vehicles and AI
- Overview of Autonomous Vehicle Levels (SAE Levels 0-5)
- Role of AI in Autonomous Driving
- Use Cases and Applications (Self-Driving Cars, Drones, Delivery Robots)
- Sensors and Perception in Autonomous Vehicles
- Types of Sensors: LIDAR, Radar, Cameras, Ultrasonics
- Sensor Fusion for Accurate Environment Perception
- AI for Perception: Object Detection, Lane Detection, and Segmentation
- Computer Vision in Autonomous Driving
- Basics of Computer Vision for Autonomous Vehicles
- Deep Learning Techniques for Object Detection (YOLO, SSD)
- Real-Time Image Processing for Vehicle Cameras
- Localization and Mapping
- Simultaneous Localization and Mapping (SLAM)
- GPS, IMU, and Dead Reckoning for Vehicle Localization
- AI Techniques for Accurate Mapping (Visual SLAM, Particle Filters)
- Path Planning and Motion Control
- Path Planning Algorithms (Dijkstra, A*, RRT)
- AI-Based Path Planning: Reinforcement Learning for Autonomous Navigation
- Motion Control Techniques (PID, MPC) for Smooth Driving
- Deep Learning for Autonomous Vehicles
- Neural Networks for Perception and Decision-Making
- Convolutional Neural Networks (CNNs) for Image Processing
- Recurrent Neural Networks (RNNs) for Sequential Data (Sensor Data)
- Reinforcement Learning in Autonomous Driving
- Fundamentals of Reinforcement Learning (Q-Learning, Policy Gradient)
- Applying RL to Decision-Making in Autonomous Vehicles
- Case Studies: Self-Learning Driving Agents
- Vehicle-to-Everything (V2X) Communication
- Communication between Vehicles (V2V), Infrastructure (V2I), and Networks (V2N)
- AI for Optimizing V2X Communication
- Safety and Traffic Management through AI-Driven V2X Systems
- Autonomous Vehicle Software Architecture
- Overview of Autonomous Driving Stacks (Apollo, Autoware)
- ROS (Robot Operating System) for Autonomous Vehicles
- AI Integration into Autonomous Vehicle Software Pipelines
- Simulation and Testing of Autonomous Vehicles
- Virtual Environments for Testing Autonomous Vehicles (CARLA, AirSim)
- AI for Simulation-Based Testing and Validation
- Reinforcement Learning for Simulated Driving Scenarios
- Safety, Ethics, and Regulations in Autonomous Driving
- AI for Safety-Critical Systems in Autonomous Vehicles
- Ethical Considerations in AI-Driven Decision Making
- Legal and Regulatory Framework for Autonomous Vehicles
- Final Project
- Develop an AI-based system for a specific task in autonomous vehicles.
Participant’s Eligibility
AI engineers, robotics engineers, data scientists, and software developers focusing on autonomous systems.
Program Outcomes
- Ability to build and deploy AI models for autonomous vehicle perception, planning, and decision-making.
- Mastery in using LIDAR, radar, and computer vision for self-driving cars.
- Hands-on experience with sensor fusion, navigation, and AI-driven control systems.
Fee Structure
Standard Fee: INR 4,998 USD 78
Discounted Fee: INR 2499 USD 39
We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!
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Key Takeaways
Program Assessment
Certification to this program will be based on the evaluation of following assignment (s)/ examinations:
| Exam | Weightage |
|---|---|
| Mid Term Assignments | 50 % |
| Project Report Submission (Includes Mandatory Paper Publication) | 50 % |
To study the printed/online course material, submit and clear, the mid term assignments, project work/research study (in completion of project work/research study, a final report must be submitted) and the online examination, you are allotted a 1-month period. You will be awarded a certificate, only after successful completion/ and clearance of all the aforesaid assignment(s) and examinations.
Program Deliverables
- Access to e-LMS
- Real Time Project for Dissertation
- Project Guidance
- Paper Publication Opportunity
- Self Assessment
- Final Examination
- e-Certification
- e-Marksheet
Future Career Prospects
- Autonomous Vehicle Engineer
- AI Specialist for AV Systems
- Perception Engineer for Self-Driving Cars
- Robotics Engineer for AVs
- Path Planning Engineer
- AV Simulation Developer
Job Opportunities
- Companies developing autonomous vehicles and self-driving car technologies.
- Research institutions focused on AI-based robotics and transportation.
- Startups and enterprises in the AV ecosystem, including logistics and smart city solutions.
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