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data-evaluation【数据与研究】Use OpenCDA's dumped YAML vehicle records for offline trajectory augmentation, evaluation-output interpretation, plotting, and non-destructive debugging.
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Route offline work on OpenCDA data dumps, trajectory labels, evaluation outputs, and debug plots here. This sub-skill is applicable when the input is a directory of frame YAML files; it does not start a simulator or alter the source tree.
scripts/generate_prediction_yaml.py
with explicit --input-root and --output-root. It writes YAML only to a
separate output tree by default; use --in-place only when source mutation is
explicitly approved.This skill covers deterministic, offline post-processing and inspection. It
requires Python with PyYAML for the bundled helper and optionally matplotlib
for plots. EvaluationManager also imports CARLA and consumes live/in-memory
vehicle-manager state; a CARLA 0.9.12 client import was checked, but no CARLA
server was verified. SUMO, ScenarioRunner, torch, and YOLOv5 runtimes are not
part of the verified environment. Do not infer a successful simulator,
co-simulation, detector, or model evaluation from an offline YAML check.