
Lucas Reljic developed core perception and machine learning infrastructure for the WATonomous/wato_monorepo and wato_wiki repositories, focusing on robust object detection and modular ML pipelines. He enhanced lidar and camera processing by refining point cloud segmentation and optimizing detection nodes using Python and ROS 2, improving data quality and localization accuracy for autonomous driving. Lucas also established a scalable deep_ros ML pipeline with modular inference containers and developer documentation, supporting seamless neural network integration. His work included comprehensive documentation updates and project management, ensuring maintainable code and clear objectives. The depth of his contributions provided a strong foundation for future development.

In Sep 2025, delivered the Deep ROS Infrastructure for ML Pipeline in ROS 2 for the WATonomous/wato_wiki project. This initiative provides modular inference node containers, a sample model farm, and developer documentation for pre-commit hook setup. The work establishes a scalable foundation for deploying, testing, and training neural networks, enabling seamless integration of perception modules into the WATonomous autonomous driving stack while improving code quality through automated pre-commit checks.
In Sep 2025, delivered the Deep ROS Infrastructure for ML Pipeline in ROS 2 for the WATonomous/wato_wiki project. This initiative provides modular inference node containers, a sample model farm, and developer documentation for pre-commit hook setup. The work establishes a scalable foundation for deploying, testing, and training neural networks, enabling seamless integration of perception modules into the WATonomous autonomous driving stack while improving code quality through automated pre-commit checks.
May 2025 monthly summary for the WATonomous/wato_wiki work stream, focused on Quest Book Documentation Updates for Winter 2025 and Spring 2025. Delivered a comprehensive documentation refresh including refactoring criteria and minimum requirements, section renumbering for clarity, and the addition of a Blog Posts section. Introduced the Spring 2025 objective (Level 5 Robo-taxi Around Campus) with explicit hardware/software objectives and a detailed scoring rubric to guide assessments and milestone tracking.
May 2025 monthly summary for the WATonomous/wato_wiki work stream, focused on Quest Book Documentation Updates for Winter 2025 and Spring 2025. Delivered a comprehensive documentation refresh including refactoring criteria and minimum requirements, section renumbering for clarity, and the addition of a Blog Posts section. Introduced the Spring 2025 objective (Level 5 Robo-taxi Around Campus) with explicit hardware/software objectives and a detailed scoring rubric to guide assessments and milestone tracking.
In April 2025, delivered a Configurable and Robust Camera Object Detection Pipeline for WAT Autonomous (WATonomous/wato_monorepo) by refactoring and optimizing the camera detection node. Improvements include enhanced parameter declarations, initialization logic, image preprocessing, and detection parsing, plus added configuration options to control visualization topic publishing for flexibility and efficiency. Strengthened robustness for correct handling of image dimensions and scaling, resulting in improved object localization accuracy. Also laid groundwork for Spring 2025 Eve Quest content in the WAT Autonomous wiki (WATonomous/wato_wiki) by introducing a boilerplate file to organize Eve Quest Book quests for S25, enabling future content creation and cataloging.
In April 2025, delivered a Configurable and Robust Camera Object Detection Pipeline for WAT Autonomous (WATonomous/wato_monorepo) by refactoring and optimizing the camera detection node. Improvements include enhanced parameter declarations, initialization logic, image preprocessing, and detection parsing, plus added configuration options to control visualization topic publishing for flexibility and efficiency. Strengthened robustness for correct handling of image dimensions and scaling, resulting in improved object localization accuracy. Also laid groundwork for Spring 2025 Eve Quest content in the WAT Autonomous wiki (WATonomous/wato_wiki) by introducing a boilerplate file to organize Eve Quest Book quests for S25, enabling future content creation and cataloging.
March 2025: Strengthened perception reliability by refining lidar processing and correcting publishing topic configuration in the Perception System. Focused on delivering concrete improvements to point cloud segmentation accuracy and ensuring robust data publishing for downstream modules.
March 2025: Strengthened perception reliability by refining lidar processing and correcting publishing topic configuration in the Perception System. Focused on delivering concrete improvements to point cloud segmentation accuracy and ensuring robust data publishing for downstream modules.
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