Overview
Led a team of four engineers in designing and developing a production-grade embedded camera system for Ford Motor Company's ADAS platforms. The system delivers real-time video processing, IR night vision imaging, video compression, and comprehensive API interfaces supporting autonomous vehicle development.
Used Agile methodologies to drive rapid development cycles, resulting in successful adoption across 60+ Ford engineering teams worldwide as the standardized camera subsystem for ADAS and autonomous vehicle research.
Key Results
- ▸Standard platform adopted by 60+ teams globally, accelerating ADAS development and reducing per-platform integration time by 40%
- ▸Developed modular tool and UI enabling specialized API calls, IR night vision, video recording, and compression—significantly enhancing functionality and performance
Architecture & Technologies
Core Technologies
- • Embedded C/C++
- • Real-time operating systems
- • Video codec optimization
- • RESTful API architecture
- • Vehicle CAN bus integration
- • Low-latency processing
Key Features
- • Multi-camera support
- • Real-time compression
- • Low-latency output
- • Automotive-grade reliability
- • Extensible API framework
- • High-performance processing
Challenges & Solutions
Modular Cross-Platform Architecture
Designed abstraction layers isolating core functionality from platform-specific implementations, enabling seamless integration across diverse Ford vehicle platforms with different hardware and software requirements.
Real-Time Performance at Scale
Implemented hardware-accelerated encoding, optimized buffer management, and parallel processing pipelines to achieve sub-100ms latency for IR and RGB video processing while maintaining automotive-grade reliability.