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Course Outline
Introduction to Autonomous and Connected EVs
- Overview of autonomous driving technology in EVs
- Understanding vehicle-to-everything (V2X) communication
- Key challenges and opportunities in connected mobility
Autonomous Driving Technologies
- Deep learning algorithms for perception and decision-making
- Sensor fusion and real-time data processing
- Path planning and motion control in autonomous EVs
V2X Communication Systems
- Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communication
- Leveraging 5G for real-time data exchange
- Integrating smart city infrastructure with autonomous EVs
Cybersecurity in Autonomous and Connected EVs
- Identifying vulnerabilities in connected vehicle networks
- Securing communication channels and data integrity
- Implementing encryption and intrusion detection systems
Software Architectures for Autonomous EVs
- Modular software design for autonomous driving systems
- Optimizing real-time processing in embedded environments
- Managing software updates and system integration
Simulation and Testing of Autonomous EV Systems
- Creating virtual environments for autonomous driving validation
- Testing V2X communication under varied conditions
- Analyzing simulation data for performance improvement
Case Studies and Real-World Applications
- Success stories from autonomous EV deployments
- Lessons learned from integrating connectivity features
- Best practices from leading automotive innovators
Summary and Next Steps
Requirements
- Proficiency in AI and machine learning concepts
- Experience with automotive software development
- Understanding of IoT and communication protocols
Audience
- AI engineers working on autonomous vehicle technologies
- Automotive industry leaders focusing on EV innovation
- IoT developers integrating connectivity solutions in EVs
14 Hours
