Autonomous Driving ML Toolchain Validation

The toolchain ran on three operating systems. Differences in file handling, driver behavior, thread scheduling, and runtime environments caused inconsistencies in labeling and timing as well as occasional instability in video analytics outputs.

New deep learning models, preprocessing variations, and expanding plugin functionality increased system complexity over time. Without structured validation and regression detection, these changes risked slowing perception teams, reducing dataset quality, and delaying ADAS development milestones.

• Validated nightly builds across Windows, Linux, and macOS and analyzed discrepancies in inference behavior, timing, and model outputs.
• Evaluated real-time video analytics pipelines for frame-level consistency, latency, stability, and accurate processing of high-volume streams.
• Verified deep learning inference behavior for new model versions, preprocessing updates, and runtime changes.
• Checked multi-format data ingestion for HDF5 or H5 files, ROS Bag files, and other sensor formats, ensuring deterministic parsing and timestamp alignment.
• Analyzed label drift and frame instability using reference sequences and repeated test runs to identify subtle behavioral shifts.
• Validated modular plugins for visualization, annotation, correction, import and export, and stream inspection across multiple releases.
• Assessed end-to-end interactions between annotation tools, correction utilities, dataset operations, and processing modules.
• Expanded test coverage to support new models, new plugins, new data formats, and evolving annotation workflows.
• Enhanced automated regression pipelines with targeted checks that surfaced cross-platform and model-related issues early.
• Performed manual validation for complex or ambiguous scenes where automated checks were insufficient.
• Remediated pipeline issues involving frame ordering, timestamp drift, preprocessing differences, and runtime variation across operating systems.