- Built system fundamental code base and set up development environment. - Conducted major integration efforts and set up vehicle environment to deploy software stacks. - Re-wrote camera pipeline to improve performance, reaching stable stable 30FPS while reduce ≈35% CPU consumption

- Designed software architecture and developed core infrastructure. - Built debugging and integration tools to enhance collaboration and streamline development processes. - Established standard practices within the repository, including coding convention, Docker and initial CI/CD set up. - Led main integration efforts and conducted major testing to ensure system reliability and performance. - Led codebase profiling and optimization for all modules, proposed and implemented GPU-based pipeline to improve inference performance of Al modules. - Oversaw repository maintenance and merge request reviews to uphold performance and scalability standards.

- Developed an SDK for integrating Al modules into Android apps for that run on the vehicle. - Proposed and implemented a full GPU pipeline (OpenGL and OpenCL backends), resulting in ≈2x faster inference with a ≈60% CPU consumption reduction. - Conducted profiling of the SDK to identify and optimize any bottlenecks, ensuring efficient operation. - Prepared on-car hardware and managed data collection process for deep learning modules.

- Created software base for Motion Planning & Control module. - Built visualization application for debugging the algorithm. - Developed two autopilot features (Blind Spot Warning) and (Forward Collision Warning). - Researched on autonomous vehicle Behavior Planning module.