Month: 2026-01. Focused on GPU compatibility and performance improvements in the autoware project. Executed a CUDA upgrade to ensure compatibility with newer NVIDIA drivers and improve GPU-related performance. Key change implemented in autowarefoundation/autoware with a signed-off commit to formalize the upgrade.

In April 2025, the team delivered cross-repo improvements across three Autoware Foundation repositories, focusing on artifacts management, transform handling, and maintenance simplification. The work emphasized reliability, documentation, and maintainability, aligning with business value by reducing configuration friction and enabling smoother transform-related operations in production deployments.

March 2025 focused on improving Docker-based installation documentation for Autoware, with targeted changes to artifact handling and run.sh usage. The team delivered a new artifacts download command and clarified the data-path option to streamline setup, while also correcting documentation after an earlier change by reverting the artifacts path and simplifying the map-path example. These efforts enhanced setup usability, onboarding efficiency, and documentation accuracy, supporting smoother deployments and fewer support questions.

December 2024 monthly summary for ros/rosdistro: Focused on updating the TensorRT CMake Module version in the Humble distribution, ensuring downstream builds reflect the updated release and aligning rosdistro metadata accordingly. The change was captured under bloom workflow, and committed to the repository to support a smoother upgrade path for TensorRT users.

In April 2024, delivered the TransFusion multi-modal 3D object detection model integration for tier4/AWML, enabling LiDAR-camera fusion on the nuScenes dataset. Implemented the new model architecture, along with training and deployment configuration files, and updated dependencies to support multi-modal processing. Impact: Improved potential detection accuracy through multi-modal fusion and established a foundation for production-ready deployment and reproducible experiments.