During February 2026, John Zhang enhanced vehicle trajectory prediction in the WATonomous/wato_monorepo repository, focusing on intent-aware predictions such as lane changes, turns, and straight paths. He refactored the prediction pipeline to leverage PoseStamped, improving timestamp accuracy and frame management within the ROS ecosystem. This technical approach increased prediction reliability and ensured better alignment with downstream planning modules. Working primarily in C++ and applying skills in algorithm development and robotics, John’s contributions addressed the need for robust, intent-driven trajectory forecasts. The depth of his work is reflected in the improved integration and readiness of the prediction system for future planning tasks.