Introduction

The automotive industry is undergoing a revolution with the rise of Autonomous Vehicles (AVs) and Connected Vehicles (CVs). These technologies are shaping the future of transportation, smart cities, and mobility-as-a-service by integrating AI, IoT, 5G, cloud computing, and advanced sensor fusion. This course provides a comprehensive, future-ready approach to understanding self-driving technology, vehicle-to-everything (V2X) communication, cybersecurity, AI-driven decision-making, and regulatory challenges.

Objectives

By the end of this course, participants will:

1. Understand the fundamental principles and levels of vehicle autonomy.
2. Learn about sensor fusion and perception systems for AVs.
3. Explore AI, machine learning, and deep learning applications in self-driving vehicles.
4. Gain insights into V2X communication, 5G, and connected mobility.
5. Understand cybersecurity risks and mitigation strategies in connected vehicles.
6. Examine real-world case studies and industry applications.
7. Develop and test AI-driven autonomous driving algorithms.

Who Should Attend?

This course is ideal for:

• Automotive Engineers & Mobility Experts developing AVs & CVs.
• AI & Machine Learning Engineers working on perception & decision-making.
• IoT & Wireless Communication Professionals integrating V2X solutions.
• Cybersecurity Experts focusing on vehicle security and data privacy.
• Urban Planners & Smart City Developers enhancing transportation networks.
• R&D Professionals & Entrepreneurs innovating in future mobility solutions.

Course Outline

Day 1: Foundations of Autonomous and Connected Vehicles

• Module 1.1: Introduction to Autonomous Vehicles (AVs)

  • SAE Levels of Autonomy (0-5)
  • Key components of an AV: Sensors, AI, Actuators, and Control Systems
  • Major players & real-world deployment: Tesla, Waymo, Cruise, Mobileye

• Module 1.2: Sensor Fusion and Perception Systems

  • LiDAR, Radar, Cameras, Ultrasonic Sensors, IMU, GNSS
  • Sensor data fusion and environmental perception
  • Challenges in sensor limitations: Weather, occlusions, sensor redundancy

• Module 1.3: AI & Machine Learning in Autonomous Vehicles

  • Deep learning for object detection & classification
  • Reinforcement learning for decision-making
  • Neural networks in perception and control

• Hands-On Session: Processing LiDAR & Camera data using Python & OpenCV

Day 2: Vehicle Connectivity & V2X Communication

• Module 2.1: Connected Vehicle Technologies

  • Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Pedestrian (V2P), Vehicle-to-Cloud (V2C)
  • 5G, DSRC, and edge computing in connected mobility
  • Role of IoT in Connected Vehicles (CVs)

• Module 2.2: Intelligent Transportation Systems (ITS) and Smart Cities

  • Connected mobility in smart cities: traffic optimization & fleet management
  • Digital Twin technology in urban planning
  • Integration of AVs & CVs into MaaS (Mobility-as-a-Service)

• Module 2.3: Cybersecurity in Connected Vehicles

  • Threat landscape: Hacking, data breaches, malware attacks
  • Security frameworks: End-to-end encryption, blockchain, secure OTA updates
  • Ethical & regulatory concerns in AVs and data privacy

• Hands-On Session: Simulating V2X communication in a virtual environment

Day 3: Path Planning, Control, and Autonomous Navigation

• Module 3.1: Localization & Mapping for AVs

  • Simultaneous Localization and Mapping (SLAM)
  • GPS, RTK, and HD Maps for high-accuracy localization
  • Case study: Waymo’s real-time localization system

• Module 3.2: Path Planning and Motion Control

  • A and Dijkstra’s Algorithm for optimal path planning*
  • Reactive vs. Predictive Motion Planning
  • Trajectory optimization & real-time control systems

• Module 3.3: Ethical and Legal Challenges of AV Deployment

  • AI decision-making in accidents: Moral dilemmas
  • Regulations & legal frameworks for AV adoption
  • Future challenges: Public acceptance and infrastructure upgrades

• Hands-On Session: Developing and testing an AI-based path planning algorithm

Day 4: Advanced AI & Simulation for Autonomous Vehicles

• Module 4.1: Reinforcement Learning for AV Control

  • Deep Q-learning and Policy Gradient methods
  • Training AI agents for self-driving tasks
  • Case study: OpenAI Gym and AV simulations

• Module 4.2: Digital Twin & Simulation for AV Testing

  • Use of virtual environments: CARLA, AirSim, and SUMO
  • Testing AV algorithms in a risk-free simulation
  • Scenario-based testing for edge cases & extreme conditions

• Module 4.3: Autonomous Fleet & Mobility-as-a-Service (MaaS)

  • Role of AVs in ride-sharing, logistics, and last-mile delivery
  • Fleet optimization using AI and cloud computing
  • Case study: Tesla’s Full Self-Driving (FSD) Beta & Robotaxi concept

• Hands-On Session: Deploying an AV simulation in CARLA or AirSim

Day 5: Future of Autonomous and Connected Vehicles

• Module 5.1: Human-Machine Interaction (HMI) in AVs

  • Voice assistants, gesture recognition, AR dashboards
  • Enhancing passenger experience in AVs
  • Safety & redundancy in AV-to-human communication

• Module 5.2: AV Business Models & Industry Disruptions

  • Future of car ownership: Subscription, shared mobility, robotaxis
  • Impact on insurance, public transport, and city planning
  • Investment & startup opportunities in AV technology

• Final Project & Certification:

  • Participants will develop a basic self-driving AI model.
  • Industry expert panel review and feedback.
  • Certification exam and participant feedback.

Conclusion and Certification

• Recap of Key Learning Points
• Q&A and Discussion on Industry Trends
• Certificate of Completion Distribution

Prerequisites:

• Basic knowledge of Python, AI, IoT, or automotive engineering.
• Familiarity with machine learning, embedded systems, and MATLAB is beneficial.
• Experience with robotics, automation, or cloud computing is a plus but not mandatory.

Course Takeaways:

✅ Gain expertise in AV and CV technologies.
✅ Work with AI-powered perception and decision-making systems.
✅ Understand V2X communication and cybersecurity in mobility.
✅ Deploy and test self-driving algorithms in simulations.
✅ Get hands-on with real-world case studies and industry applications.