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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
