June 2025 monthly summary focusing on stabilizing runtime behavior, simplifying configuration, and reducing maintenance burden across Tier4 repos. Key outcomes include a system-stability improvement in aip_launcher by disabling the Velodyne monitor and refining radar data handling, plus a configuration cleanup in autoware_launch to streamline obstacle planning. These efforts deliver clearer deployment expectations, lower risk of conflicts, and more maintainable code paths.

Monthly summary for 2025-05 focusing on key features delivered, major bugs fixed, overall impact, and technologies demonstrated. Highlights business value, robustness, and automation improvements achieved across two repositories.

March 2025 monthly summary focusing on key accomplishments and business value across tier4/autoware_launch and tier4/aip_launcher. Delivered core feature improvements and hardware integration that enhance safety, reliability, and sensor interoperability. Major outcomes include refined obstacle decision logic and updated Hesai Nebula v0.2.x support, improving data fidelity and processing latency for safer autonomous operation.

February 2025 monthly summary: Focused on strengthening EKF Localizer robustness on gradient roads by tuning z-axis processing deviation (z_filter_proc_dev) across two repositories. In autowarefoundation/autoware.core, increased z_filter_proc_dev from 1.0 to 5.0 to better accommodate large road inclines, improving vertical localization accuracy. In tier4/autoware_launch, applied a related tuning to EKF Localizer z-axis processing deviation for gradient-road localization, enhancing localization stability on steep gradients. These changes reduce vertical drift, improve route fidelity on challenging terrain, and contribute to safer, more reliable autonomous driving. Business value: improved navigation reliability on hilly terrain, reduced need for manual adjustments, and stronger safety assurances in gradient environments.

January 2025 — Tier4/aip_launcher: Key feature delivered with XX1 Gen2 Sensor Integration Enhancement. Implemented lidar and GNSS configuration updates and introduced a Python utility to convert Septentrio attitude Euler data to ROS quaternion format, enhancing orientation data processing and sensor integration. No major bugs fixed this month. Overall impact: improved orientation accuracy, robust ROS integration, and smoother downstream data pipelines, supporting more reliable autonomous operation. Technologies demonstrated include Python scripting, ROS data formats (quaternions), sensor configuration, and Git-based release workflow.