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Datasets:
luuuulinnnn
/
Nurec_eval_v2

Tasks:
Robotics
Modalities:
3D
Size:
n<1K
Tags:
autonomous-driving
nurec
closed-loop-eval
alpasim
alpamayo
mads
License:
Dataset card Data Studio Files
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Nurec_eval_v2 / README.md
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luuuulinnnn
Add 59 OOD-longtail USDZ scenes with MADS HD-map (trajdata-compatible)
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metadata 
license: cc-by-4.0
task_categories:
  - robotics
tags:
  - autonomous-driving
  - nurec
  - closed-loop-eval
  - alpasim
  - alpamayo
  - mads
size_categories:
  - n<1K

Nurec_eval_v2 — 59 OOD-longtail scenes (MADS schema, trajdata-compatible)

59 NRE 26.02 closed-loop eval scenes with HD-map data packaged in the MADS schema that trajdata's populate_vector_map() reads. Drop-in usable by NVlabs/alpasim — when you call Artifact(usdz).map, you get back a fully-populated VectorMap (lanes / road edges / wait lines) and the driver model gets lane-aware input.

What changed vs v1 (Nurec_eval)

v1 (Nurec_eval) v2 (this)
Map artifact location map_data/cf_*.parquet, dw_lane.parquet, ... map_data/{lane,road_boundary,association,wait_line,traffic_sign,clip}.parquet
Schema NRE-internal ClipGT format MADS format (trajdata expects this)
alpasim Artifact.map loads it ❌ KeyError on lane.parquet ✅ Loads ~30–280 lanes / 10–30 road edges per scene
Driver model has map context ❌ map = None ✅ VectorMap populated

If you only need raw NRE ClipGT data, use luuuulinnnn/Nurec_eval (v1).

What's inside each pai_<uuid>.usdz

File Purpose
default.usda, parsed_config.yaml, data_info.json, metadata.yaml, datasource_summary.json Scene metadata
checkpoint.ckpt NRE-26.02 Gaussian-splat reconstruction weights (~700–800 MB)
mesh.ply, mesh.usd Poisson reconstruction mesh
ground_mesh.ply Ground mesh (for collision_with_ground / offroad-by-ground)
rig_trajectories.json, rig_trajectories.usda Ego recorded trajectory (used by route_generator_type=RECORDED)
sequence_tracks.json, sequence_tracks.usda Other actors' recorded trajectories (replay mode for trafficsim)
map_data/clip.parquet MADS: scene metadata
map_data/lane.parquet MADS: lanes (key.map_id, Lane.left_rail, Lane.right_rail)
map_data/road_boundary.parquet MADS: road edges (RoadBoundary.location polyline)
map_data/association.parquet MADS: NEXT_LANE / PREVIOUS_LANE / LEFT_LANE / RIGHT_LANE relations
map_data/wait_line.parquet MADS: wait lines (from NRE crosswalks)
map_data/traffic_sign.parquet MADS: traffic signs (synthetic; NRE recon doesn't ship signs)

Scene composition — 59 scenes from 8 OOD-longtail buckets

Category Scenes
Animals / Birds / Roadkill 7
Complex Intersection Interaction 8
Cyclists & Micromobility Complex 8
Emergency Incident Scene 7
Pedestrian Density / Close Proximity 8
Road Debris / Safety Traces 5
Special / Uncommon Vehicle Behavior 8
Work Zones / Temp Traffic Control 8
⚠️ Broken route (waypoint folds back) 1
Total usable 58 scenes

Scene → category mapping is in category_uui.json.

Usage with AlpaSim 

alpasim_wizard \
  deploy=local topology=1gpu driver=<your_model> \
  +physics=disabled +vehicle=custom \
  scenes.path=/path/to/Nurec_eval_v2 \
  scenes.scenes_csv=/path/to/Nurec_eval_v2/sim_scenes.csv \
  scenes.suites_csv=/path/to/Nurec_eval_v2/sim_suites.csv \
  ...

Map will be auto-loaded by Artifact.map and fed to the driver as part of scenario context.

How the MADS files were produced

The included convert_cf_to_mads.py converts NRE ClipGT parquets (cf_crosswalks.parquet, cf_lane_topology_node.parquet, cf_road_boundary.parquet, dw_lane.parquet, lane_chunk.parquet, lane_rail.parquet) into the 6 MADS files.

Then inject_mads_map.py zips them into the USDZ at map_data/.

Key mappings:

  • dw_lane.currentId + chunkIndices + lane_chunk.left/right → lane.parquet (Lane.left_rail, Lane.right_rail)
  • cf_road_boundary.road_boundary_polyline → road_boundary.parquet (RoadBoundary.location)
  • dw_lane.lgwm_lane_continuation_array → association.parquet (NEXT_LANE / PREVIOUS_LANE)
  • lane_rail.lane_indices → association.parquet (LEFT_LANE / RIGHT_LANE)
  • cf_crosswalks.reference_line → wait_line.parquet (WaitLine.location, WaitLine.category = "CROSSWALK")

Source