Autonomous Formula SAE – Redback Racing
Built real-time perception and autonomy systems for UNSW’s Formula SAE driverless race car using YOLOv7, ZED depth sensing, and NVIDIA Jetson compute.
Computer Vision & Perception Engineer
2021–2023
Real-Time Perception on NVIDIA Jetson
I integrated a ZED stereo camera with an NVIDIA Jetson Orion to generate depth-aware detections in real time. This setup served as the backbone for cone detection—a core requirement for autonomous navigation on the FSAE track.
YOLOv7 for High-Speed Object Detection
Using PyTorch and YOLOv7, I implemented a real-time cone detection system optimized for low-latency inference on Jetson hardware. This involved dataset prep, model training, hyperparameter tuning, and edge deployment.
"Building perception systems for an autonomous race car pushed my understanding of real-time vision, sensor fusion, and robotics integration to a whole new level."