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AI in Autonomous Vehicles Course

Original price was: INR ₹4,998.00.Current price is: INR ₹2,499.00.

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.

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Aim

This program teaches the essential AI technologies and methodologies that enable autonomous vehicles (AVs) to perceive, make decisions, and navigate complex environments. Participants will gain hands-on experience with deep learning, sensor fusion, and path planning techniques, critical for building AV systems.

Program Objectives

  • Learn Core AI Technologies: Understand the AI technologies that power self-driving vehicles.
  • Master Sensor Fusion: Learn how to combine data from sensors like LIDAR, radar, and cameras.
  • Computer Vision for AVs: Implement computer vision techniques for detecting roads, objects, and lane markings.
  • Reinforcement Learning: Apply reinforcement learning for real-time decision-making in AVs.
  • Hands-On AV System Building: Gain practical experience in building AI-driven systems for autonomous vehicles.

Program Structure

Module 1: Introduction to Autonomous Vehicles and AI

  • Overview of autonomous vehicle levels (SAE Levels 0-5).
  • Role of AI in autonomous driving systems.
  • Applications: Self-driving cars, drones, and delivery robots.

Module 2: Sensors and Perception in Autonomous Vehicles

  • Types of sensors: LIDAR, Radar, Cameras, Ultrasonics.
  • Sensor fusion for accurate environment perception.
  • AI techniques for object detection, lane detection, and segmentation.

Module 3: Computer Vision in Autonomous Driving

  • Basics of computer vision for autonomous vehicles.
  • Deep learning for object detection (YOLO, SSD).
  • Real-time image processing for vehicle cameras.

Module 4: Localization and Mapping

  • Simultaneous Localization and Mapping (SLAM) for AVs.
  • GPS, IMU, and dead reckoning for vehicle localization.
  • AI techniques for accurate mapping (Visual SLAM, Particle Filters).

Module 5: Path Planning and Motion Control

  • Path planning algorithms: Dijkstra, A*, RRT.
  • Reinforcement learning for AI-based path planning and navigation.
  • Motion control techniques (PID, MPC) for smooth driving.

Module 6: Deep Learning for Autonomous Vehicles

  • Neural networks for perception and decision-making.
  • CNNs for image processing, RNNs for sequential sensor data analysis.

Module 7: Reinforcement Learning in Autonomous Driving

  • Introduction to reinforcement learning: Q-learning, Policy Gradient.
  • Case studies of self-learning driving agents.

Module 8: Vehicle-to-Everything (V2X) Communication

  • Communication between vehicles (V2V), infrastructure (V2I), and networks (V2N).
  • AI for optimizing V2X communication and improving traffic management.

Module 9: Autonomous Vehicle Software Architecture

  • Overview of AV software stacks (Apollo, Autoware).
  • ROS (Robot Operating System) for autonomous vehicles.
  • AI integration into AV software pipelines.

Module 10: Simulation and Testing of Autonomous Vehicles

  • Virtual environments for testing AVs (CARLA, AirSim).
  • Reinforcement learning for simulated driving scenarios.

Module 11: Safety, Ethics, and Regulations in Autonomous Driving

  • AI for safety-critical systems in AVs.
  • Ethical considerations in AI-driven decision-making.
  • Legal and regulatory framework for AV systems.

Final Project

  • Develop an AI-based system for a specific task in autonomous vehicles, such as path planning, object detection, or sensor fusion.

Participant Eligibility

  • AI Engineers: Professionals working on AI and machine learning models for autonomous systems.
  • Robotics Engineers: Specialists developing robotics solutions for AVs.
  • Data Scientists: Focusing on applying AI techniques to autonomous driving problems.
  • Software Developers: Working on integrating AI into autonomous vehicle systems.

Program Outcomes

  • AI Model Development: Ability to build and deploy AI models for AV perception, planning, and decision-making.
  • Master Sensor Fusion: Proficiency in using LIDAR, radar, and computer vision technologies for self-driving cars.
  • Hands-On Experience: Gain practical skills in navigation, sensor fusion, and AI-driven control systems for AVs.

Program Deliverables

  • Access to e-LMS: Full access to course materials and resources online.
  • Real-Time Projects: Develop hands-on projects in AI-driven autonomous vehicle systems.
  • Project Guidance: Mentorship and support during your project work.
  • Research Paper: Opportunity to publish a research paper on AV systems and AI.
  • Final Examination: Certification awarded based on mid-term assignments and final project submissions.
  • e-Certification: Awarded upon successful completion of the program.

Future Career Prospects

  • Autonomous Vehicle Engineer: Develop AV systems for self-driving cars and robotics.
  • AI Specialist for AV Systems: Focus on AI model development for autonomous vehicle software.
  • Perception Engineer for Self-Driving Cars: Work on object detection and environmental perception for AVs.
  • Robotics Engineer for AVs: Build robotics systems that support autonomous navigation.
  • Path Planning Engineer: Design and optimize navigation systems for AVs.
  • AV Simulation Developer: Create simulation environments to test autonomous driving systems.

Job Opportunities

  • Companies developing autonomous vehicles: Tesla, Waymo, Cruise, and others working on self-driving car technology.
  • Research institutions: Focused on AI-based robotics, transportation, and AV systems.
  • Startups and enterprises: In the AV ecosystem, including logistics, smart cities, and delivery services.
MODE

Online/ e-LMS
TYPE

Self Paced
LEVEL

Moderate
DURATION

3 Weeks
Google Product Category

2094

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Certification

  • Upon successful completion of the workshop, participants will be awarded a Certificate of Completion, validating their skills and knowledge in advanced AI ethics and regulatory frameworks. This certification can be added to your LinkedIn profile or shared with employers to demonstrate your commitment to ethical AI practices.

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