| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Advanced |
| Duration | 3 Weeks |
| Certification | NSTC e-Certification + e-Marksheet |
| Fee | ₹5499 / $59 |
| Tools | ROS (Robot Operating System) Python & C++ for Real-time Systems Computer Vision: OpenCV, YOLO, Segmentation CARLA / Gazebo Simulators TensorRT for Edge Inference |
About the AI in Autonomous Vehicles Course
The AI for Autonomous Vehicles and Intelligent Mobility course bridges the gap between automotive engineering, computer vision, and real-time decision intelligence. As transportation shifts toward full autonomy, the ability to process high-fidelity sensor data and execute safety-critical decisions in milliseconds has become the industry standard.
This program addresses the technical demands of 2026, covering the end-to-end stack of self-driving technology—from raw sensor fusion (LiDAR, Radar, Camera) to Edge AI deployment. Participants will move beyond theory, engaging with the complexities of path planning, SLAM, and regulatory frameworks required for industrial-scale intelligent transport systems.
Program Highlights
- Edge-to-Cloud Integration: Focuses on low-latency Edge inference and embedded systems hardware critical for vehicle safety.
- Full-Stack Perception: Covers deep learning for object detection, semantic segmentation, and behavioral recognition in dynamic environments.
- Safety-First Framework: Dedicated modules on functional safety (ISO 26262), cybersecurity, and ethical AI accountability.
- Real-World Simulation: Hands-on projects using virtual validation workflows and high-definition (HD) mapping strategies.
Course Curriculum
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
Tools, Techniques, or Platforms Covered
ROS (Robot Operating System) Python & C++ for Real-time Systems Computer Vision: OpenCV, YOLO, Segmentation CARLA / Gazebo Simulators TensorRT for Edge Inference
Who Should Attend & Prerequisites
- Automotive engineers and roboticists
- Embedded system developers and AI researchers
- Transportation planners and policy analysts
- Postgraduate students in Data Science, AI, or Automotive Engineering
Certification
