
Developed an end-to-end YOLO-based object detection and CARLA data pipeline within the OpenHUTB/nn repository, integrating real-time perception with autonomous vehicle simulation. The work included building the YOLO core framework in Python, handling model loading, preprocessing, inference, and post-processing, while adding TensorBoard monitoring for observability. CARLA simulator integration enabled autonomous vehicle testing with live RGB camera streams, and the main control loop combined object detection with trajectory planning and automated emergency braking logic. Configuration management was improved through centralized settings and CLI support, and stability was enhanced by resolving Python syntax errors and refactoring modules for better performance and maintainability.
March 2026 — OpenHUTB/nn: End-to-end YOLO-based object detection and CARLA data pipeline integrated into the main control loop, with observability, configuration management, and a formal release.
March 2026 — OpenHUTB/nn: End-to-end YOLO-based object detection and CARLA data pipeline integrated into the main control loop, with observability, configuration management, and a formal release.

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