🚗 5-Day Training Program: Connected & Automated Vehicles (CAVs)
🎯 Target Audience
- Telecom & IT professionals
- Automotive engineers
- AI/ML engineers
- Government & policy professionals
- Students (Engineering/Management)
🎓 Training Objective
To provide a holistic understanding of CAV ecosystems, including connectivity, automation, AI, sensors, and real-world deployment challenges.
📅 Day 1: Introduction to CAV Ecosystem & Industry Trends
📘 Topics Covered
- Evolution of automotive industry (CASE: Connectivity, Automation, Sharing, Electrification)
- Introduction to Connected & Automated Vehicles (CAVs)
- Levels of automation (SAE Levels 0–5)
- CAV architecture overview:
- Perception system
- Path planning
- Actuation system
- Key players (OEMs, Tech companies, Startups)
- Industry use cases:
- Robo-taxis
- Autonomous trucking
- Smart mobility
🧪 Activities
- Case discussion: Tesla vs Waymo approaches
- Group exercise: Identify CAV opportunities in India
📅 Day 2: Connectivity Technologies & V2X Communication
📘 Topics Covered
- Connectivity fundamentals:
- V2V (Vehicle-to-Vehicle)
- V2I (Vehicle-to-Infrastructure)
- V2X (Vehicle-to-Everything)
- Technologies:
- DSRC vs C-V2X vs 5G
- Role of telecom networks in CAV
- Edge computing & cloud integration
- Cybersecurity in connected vehicles
🧪 Activities
- Simulation: Smart traffic signal coordination
- Discussion: Role of 5G in autonomous driving
📅 Day 3: Sensors, Perception & AI in CAV
📘 Topics Covered
- Sensor technologies:
- LiDAR
- RADAR
- Cameras
- Ultrasonic sensors
- Sensor fusion techniques
- Computer vision basics:
- Object detection
- Tracking
- Segmentation
- AI/ML models for perception
- Challenges:
- Weather conditions
- False positives/negatives
🧪 Activities
- Demo: Object detection using AI
- Case Study: Tesla Autopilot vs Waymo perception systems
📅 Day 4: Path Planning, Control & Decision Making
📘 Topics Covered
- Path planning fundamentals:
- Route planning vs trajectory planning
- Decision-making systems:
- Rule-based
- Probabilistic
- AI-based systems
- Motion control:
- Kinematic control
- Model Predictive Control (MPC)
- Ethical considerations:
- Moral dilemmas (AI decisions)
- Real-world driving scenarios:
- Urban traffic
- Highway automation
🧪 Activities
- Simulation: Autonomous vehicle navigating traffic
- Role-play: Ethical decision-making scenarios
📅 Day 5: Testing, Deployment, Business Models & Future Outlook
📘 Topics Covered
- Verification & Validation:
- Simulation testing
- Real-world testing
- Safety & regulatory frameworks
- Deployment challenges:
- Infrastructure
- Legal issues
- Public acceptance
- Business models:
- Robo-taxi
- Mobility-as-a-Service (MaaS)
- ROI estimation & cost factors
- Future trends:
- Smart cities
- AI-driven mobility
- Autonomous logistics
🧪 Activities
- Capstone Project:
- Design a CAV-based startup/business model
- Presentation & evaluation
📊 Key Deliverables
- 📘 Training handbook (based on CAV fundamentals)
- 📑 Case studies (Tesla, Waymo, Uber)
- 🧾 Business model & ROI template
- 🛠 Simulation exercises
📈 Learning Outcomes
By the end of this course, participants will:
- Understand end-to-end CAV architecture
- Gain knowledge of AI, sensors, and connectivity
- Evaluate business and deployment strategies
- Design real-world autonomous mobility solutions
Master the fundamentals of Connected and Automated Vehicles (CAVs) in this intensive 5-day training program. Learn about V2X communication, AI-powered perception, sensor fusion, autonomous driving technologies, path planning, and real-world deployment strategies. Designed for telecom, IT, automotive, and AI professionals, this course covers industry use cases, business models, ROI estimation, and future mobility trends including smart cities and robo-taxis.
