Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI in Autonomous Vehicles explains the machine intelligence that lets a car drive itself. You learn the autonomous-driving stack: perception with cameras, LiDAR and radar; sensor fusion and localisation to build a picture of the world; prediction of other road users; and path planning and control to act safely. The course covers the deep-learning methods behind perception, the levels of autonomy, and the profound safety and validation challenges of putting AI in charge of a vehicle. You finish with a clear understanding of how self-driving systems work. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers AI in autonomous vehicles — perception, sensor fusion, localisation, path planning and the decision-making behind self-driving systems.
📋 Course Objectives
1. Explain the autonomous-driving perception stack.
2. Apply sensor fusion and localisation.
3. Predict the behaviour of other road users.
4. Understand path planning and control.
5. Address safety, validation and levels of autonomy.
👥 Who Should Enroll?
• Automotive and robotics engineers
• Perception and ML developers
• Autonomous-systems researchers
• Students of self-driving technology
🚀 Key Learning Outcomes
• An understanding of the self-driving stack.
• The ability to reason about autonomous systems.
• A foundation in autonomous-vehicle AI.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Module 1 — Foundations of Autonomous Vehicles and Intelligent Mobility
Introduction to AVs and smart transportation ecosystems • Evolution from ADAS to Level 5 full autonomy • Operational Design Domains (ODD) and system boundaries • Role of AI in perception, planning, and control workflows
Module 2 — Sensors, Data Acquisition, and Vehicle Perception
Camera, LiDAR, Radar, Ultrasonic, and GPS hardware overview • Multi-modal sensor fusion for robust environmental understanding • Sensor calibration, synchronization, and data acquisition • Perception challenges in adverse weather and dynamic conditions
Module 3 — Machine Learning and Computer Vision for Autonomous Driving
Deep learning architectures for mobility systems • Object detection, lane tracking, and semantic segmentation • Scene understanding and human behavior recognition • Reliability, safety, and model evaluation metrics
Module 4 — Localization, Mapping, and Navigation
Principles of vehicle positioning and localization • Simultaneous Localization and Mapping (SLAM) techniques • High-Definition (HD) maps and route planning layers • Navigation in structured urban and unstructured off-road environments
Module 5 — Path Planning, Control, and Decision Intelligence
Motion planning and trajectory generation algorithms • Vehicle control: steering, braking, and acceleration feedback loops • AI decision-making in complex, high-traffic scenarios • Obstacle avoidance and road-user interaction modeling
Module 6 — Edge AI, Embedded Systems, and Real-Time Deployment
Real-time computing and low-latency architecture • Embedded AI hardware (NVIDIA Jetson, SoC) and software stacks • Edge inference optimization and model pruning • System integration: power, memory, and thermal constraints
Module 7 — Safety, Security, and Regulatory Considerations
Functional safety and fail-safe system design • Cybersecurity for connected and autonomous fleets • Ethical decision-making and AI accountability • Regulatory frameworks, global standards, and validation
Module 8 — Applications, Case Studies, and Future Trends
Case studies: Autonomous cars vs. delivery robots • AI in driver monitoring and advanced ADAS • Virtual validation and hardware-in-the-loop (HIL) workflows • Future: V2X (Vehicle-to-Everything) and connected mobility
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | ROS (Robot Operating System) |
| Covered Tool / Platform | Python & C++ for Real-time Systems |
| Covered Tool / Platform | Computer Vision: OpenCV, YOLO, Segmentation |
| Covered Tool / Platform | CARLA / Gazebo Simulators |
| Covered Tool / Platform | TensorRT for Edge Inference |
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