• Developed sophisticated machine learning models that analyze engine performance data to predict component failures, optimize fuel efficiency, and enhance overall vehicle performance.
The system processes real-time telemetry data and provides actionable insights to both engineers and drivers.
Key Features
- • Real-time engine performance analysis
- • Component failure prediction with 92% accuracy
- • Fuel efficiency optimization algorithms
- • Adaptive learning from driver behavior
- • Integration with vehicle control systems
- • Cloud-based model training and deployment
Technical Implementation
The solution utilizes TensorFlow and PyTorch for deep learning models, deployed on Azure ML platform. Real-time inference is handled through edge computing devices in vehicles, with continuous model updates via OTA. The system integrates with existing ECU systems through CAN bus protocols.
Impact & Results
The ML system has improved fuel efficiency by 12%, reduced unexpected engine failures by 40%, and provided valuable insights for future engine design iterations. It has become a key differentiator in McLaren's vehicle performance offerings.
