Datasets:

shdragron
/

pdm-lite-train-R1_Normal

---
pretty_name: PDM-Lite R1_Normal
tags:
- autonomous-driving
- carla
- bench2drive
- end-to-end-driving
- multi-camera
- lidar
size_categories:
- 100K<n<1M
configs:
- config_name: preview
  default: true
  data_files:
  - split: train
    path: preview/*.parquet
---
# PDM-Lite R1_Normal
Expert driving in **CARLA 0.9.15** on the full route set of carla_garage's **PDM-Lite** data (CARLA Leaderboard 2.0
scenarios, the scenario set Bench2Drive also uses), driven by the rule-based PDM-Lite expert and recorded with **two
camera rigs at once**: BEVAD's six nuScenes-style cameras and the eight-camera **PCCR R1** rig, plus a 32-channel
LiDAR and the simulator's labels at 5 Hz.

**Collection in progress** (2026-10-30): 4467 of 5545 routes attempted, 4100 successful routes uploaded so far; new routes are added as they finish. The numbers below are those of 2026-10-30.

![Front cameras of six routes, real time](samples/front_cameras.gif)

| | |
|---|---|
| Routes (successful episodes) | **4,100** |
| Frames (5 Hz) | **613,571** (34.1 h of driving) |
| Images | 8,589,994 (14 per frame: 6 BEVAD + 8 R1), JPEG 800 x 448 |
| Size | 383 GB in 4,100 tar files |
| Scenario types | 38 scenario types + routes without a scenario (`noScenarios`) |
| Towns | Town01, Town02, Town03, Town04, Town05, Town10HD, Town12, Town13 |
| Weather | 22 CARLA presets; 48% of the routes in night mode (vehicle lights on, carla_garage's rule: low sun, heavy cloud or fog) |
| Ego vehicle | `vehicle.lincoln.mkz_2020` |

The **Dataset Viewer** above shows the `preview` table: one key frame of up to four routes per scenario, with the front
camera, both surround views and the expert's control. `index.csv` lists every uploaded route (the viewer reads one
format per repository, so it is not a viewer table).

## Samples

All six cameras of BEVAD (`rgb`) and all eight of R1 (`rgb_r1`) at the same instant; in the middle of the R1 view the
LiDAR (grey) and the world-state boxes in a bird's-eye view, forward up (white: ego, blue: vehicles, orange:
pedestrians, aqua: bicycles and motorcycles, grey outline: static objects; rings every 10 m).

**DynamicObjectCrossing · Town05 · WetCloudyNoon** (`data/lb1_split/DynamicObjectCrossing/Town05_Scenario3_44.tar`, frame 203)

![BEVAD six cameras](samples/DynamicObjectCrossing_Town05_WetCloudyNoon_bevad6.jpg)

![R1 eight cameras with LiDAR BEV](samples/DynamicObjectCrossing_Town05_WetCloudyNoon_r1_8cam_bev.jpg)

More scenes (BEVAD cameras and the bird's-eye view, ±40 m):

| BEVAD cameras | LiDAR + boxes |
|---|---|
| ![SignalizedJunctionLeftTurn](samples/SignalizedJunctionLeftTurn_Town12_HardRainNight_bevad6.jpg) | ![SignalizedJunctionLeftTurn BEV](samples/SignalizedJunctionLeftTurn_Town12_HardRainNight_bev.png) |
| SignalizedJunctionLeftTurn · Town12 · HardRainNight · frame 73 | `data/50x38_Town12/SignalizedJunctionLeftTurn/1002_0.tar` |
| ![ConstructionObstacle](samples/ConstructionObstacle_Town12_MidRainyNoon_bevad6.jpg) | ![ConstructionObstacle BEV](samples/ConstructionObstacle_Town12_MidRainyNoon_bev.png) |
| ConstructionObstacle · Town12 · MidRainyNoon · frame 58 | `data/50x38_Town12/ConstructionObstacle/2266_0.tar` |
| ![HighwayCutIn](samples/HighwayCutIn_Town13_ClearSunset_bevad6.jpg) | ![HighwayCutIn BEV](samples/HighwayCutIn_Town13_ClearSunset_bev.png) |
| HighwayCutIn · Town13 · ClearSunset · frame 40 | `data/50x36_Town13/HighwayCutIn/1283_16.tar` |

The `samples/` folder has the same views for all six scenes.

## Sensors

Positions in CARLA's vehicle frame of the actor (x forward, y right, z up; metres, degrees). Every camera is
rendered at the listed resolution and stored at 800 x 448, JPEG quality 75.

| camera | folder | x, y, z | yaw | pitch | FoV | rendered |
|---|---:|---:|---:|---:|---:|---:|
| `CAM_FRONT` | `rgb` | 0.60, 0.00, 1.60 | 0 | 5 | 90 | 1600 x 900 |
| `CAM_FRONT_LEFT` | `rgb` | 0.27, -0.55, 1.60 | -55 | 0 | 90 | 1600 x 900 |
| `CAM_FRONT_RIGHT` | `rgb` | 0.27, 0.55, 1.60 | 55 | 0 | 90 | 1600 x 900 |
| `CAM_BACK` | `rgb` | -2.00, 0.00, 1.60 | 180 | -5 | 90 | 1600 x 900 |
| `CAM_BACK_LEFT` | `rgb` | -0.32, -0.55, 1.60 | -110 | -5 | 90 | 1600 x 900 |
| `CAM_BACK_RIGHT` | `rgb` | -0.32, 0.55, 1.60 | 110 | -5 | 90 | 1600 x 900 |
| `CAM_BUMPER_FRONT` | `rgb_r1` | 2.36, 0.00, 0.68 | 0 | 0 | 65 | 1280 x 720 |
| `CAM_FENDER_FRONT_RIGHT` | `rgb_r1` | 1.09, 0.99, 0.72 | 41 | 0 | 65 | 1280 x 720 |
| `CAM_SIDE_RIGHT` | `rgb_r1` | -0.22, 0.74, 1.40 | 90 | 0 | 65 | 1280 x 720 |
| `CAM_FENDER_BACK_RIGHT` | `rgb_r1` | -1.57, 0.99, 0.84 | 135 | 0 | 65 | 1280 x 720 |
| `CAM_BUMPER_BACK` | `rgb_r1` | -2.44, 0.00, 0.64 | 180 | 0 | 65 | 1280 x 720 |
| `CAM_FENDER_BACK_LEFT` | `rgb_r1` | -1.63, -0.99, 0.86 | -135 | 0 | 65 | 1280 x 720 |
| `CAM_SIDE_LEFT` | `rgb_r1` | -0.20, -0.78, 1.40 | -90 | 0 | 65 | 1280 x 720 |
| `CAM_FENDER_FRONT_LEFT` | `rgb_r1` | 1.23, -0.95, 0.78 | -43 | 0 | 65 | 1280 x 720 |

LiDAR `LIDAR_TOP` (`point_cloud/`, LAZ): 32 channels, range 100 m, vertical FoV
-30° to +10°, at (-0.46, 0.0, 1.86). IMU and GNSS
are part of the episode metadata. The full rig with every attribute is `meta/rig_r1_normal.json`.

## What a frame contains

Each route is one episode in bevad-sim's `EpisodeIo` format: `ep_data.zstd` (zstandard-compressed pickle of a
`CoreContainer`) next to the sensor folders. Arrays have a leading batch axis of 1, then time (5 Hz), then the slot.

| field | content |
|---|---|
| `world_state` | every tracked object incl. the ego: 4x4 pose (world), size, category (car, truck, bus, ambulance, pedestrian, bicycle, motorcycle, construction, roadway item), attribute (parking, driving, emergency, open door, ...), track id, visibility, validity |
| `tce` | traffic lights and signs: pose, size, control box, state (red / yellow / green / off, sign type) |
| `odometry` | GNSS, compass, angular velocity, acceleration |
| `action.simulator_control` | the expert's throttle, steer, brake per frame |
| `routing_information` | target route and route commands, navigation goal per frame |
| `rgb`, `rgb_r1`, `point_cloud` | per camera / LiDAR: file per frame (named by simulator frame id), timestamps, intrinsics, extrinsics (ego -> sensor) |
| `episode_meta` | scenario type, town, route file, weather parameters, ego size |

Labels and point clouds are in the ego frame of bevad-sim: x forward, y left, z up, origin at the centre of the ego's
bounding box (0.75 m above the ground for this car). Static map data is not stored; the towns are CARLA's.

## Layout

```
data/<set>/<scenario>/<route>.tar      one route; unpacks to <set>/<scenario>/<route>/
    summary.json                         seed, frames, route score, sizes
    collect.log                          the collection log of that route
    <route>-<id>/ep_data.zstd            labels (see above)
    <route>-<id>/rgb/CAM_*/<frame>.jpg   BEVAD cameras
    <route>-<id>/rgb_r1/CAM_*/<frame>.jpg  R1 cameras
    <route>-<id>/point_cloud/LIDAR_TOP/<frame>.laz
index.csv                              uploaded routes: order, set, scenario, town, weather, night, seed, frames, score, size, tar
meta/rig_r1_normal.json                sensor rig
meta/routes_index.csv                  all 5545 route files with seed and weather
preview/, samples/                     the viewer table and the figures of this card
```

## Loading

```python
from huggingface_hub import hf_hub_download
import tarfile
p = hf_hub_download("shdragron/pdm-lite-train-R1_Normal", "data/lb1_split/DynamicObjectCrossing/Town05_Scenario3_44.tar", repo_type="dataset")
tarfile.open(p).extractall("pdm_lite")
```

```python
# pip install -e <github.com/dmholtz/bevad>/bevad-sim    (bevad-sim reads the episode format)
import glob
from bevad_sim.data_interface.episode_io import EpisodeIo
ep_dir = glob.glob("pdm_lite/lb1_split/DynamicObjectCrossing/Town05_Scenario3_44/*/")[0]
ep = EpisodeIo.read_episode(ep_dir, load_payload=False)       # labels only; images and LiDAR stay on disk
ws = ep.world_state
print(ws.transform.shape)                                     # (1, frames, objects, 4, 4)
cam = ep.rgb.sensor_names.index("CAM_FRONT")
frame_id = int(ep.rgb.frame_ids[0, 0, cam])                   # -> {ep_dir}rgb/CAM_FRONT/<frame_id>.jpg
```

`ep_data.zstd` is a pickle: open it only from this repository (or other sources you trust).

## Collection

- **Expert**: PDM-Lite (carla_garage, `team_code/autopilot.py`) through BEVAD's bevad-sim port, with carla_garage's
  red-light stop distance (6 m) and vehicle lights in night mode restored, so it drives as carla_garage's original.
- **Routes**: every route file of carla_garage's PDM-Lite data set (`50x38_Town12`, `50x36_Town13`, `lb1_split`;
  5545 files, one route each), in carla_garage's shuffled order (seed 42); the traffic-manager seed counts up by
  one per route.
- **Weather**: one of the first 22 CARLA presets per route, as carla_garage's `shuffle_weather`, fixed by the route seed.
- **Episodes**: simulator at 20 Hz, recorded every 4th tick (5 Hz). A route that fails (scenario could not be spawned,
  simulator crash, 90 min time-out) gets one more try on a fresh server; only routes whose episode succeeded are here.

## Distribution

<details><summary>Sets and towns</summary>

| set | town | routes | frames | hours |
|---|---:|---:|---:|---:|
| 50x36_Town13 | Town13 | 1376 | 221,095 | 12.3 |
| 50x38_Town12 | Town12 | 1428 | 218,363 | 12.1 |
| lb1_split | Town01 | 90 | 10,333 | 0.6 |
| lb1_split | Town02 | 58 | 6,606 | 0.4 |
| lb1_split | Town03 | 213 | 30,771 | 1.7 |
| lb1_split | Town04 | 399 | 45,131 | 2.5 |
| lb1_split | Town05 | 426 | 63,029 | 3.5 |
| lb1_split | Town10HD | 110 | 18,243 | 1.0 |

</details>

<details><summary>Scenarios (39)</summary>

| scenario | routes | frames | hours |
|---|---:|---:|---:|
| VehicleTurningRoute | 439 | 81,148 | 4.5 |
| noScenarios | 290 | 28,416 | 1.6 |
| SignalizedJunctionLeftTurn | 269 | 21,195 | 1.2 |
| ControlLoss | 209 | 39,031 | 2.2 |
| DynamicObjectCrossing | 191 | 38,722 | 2.2 |
| SignalizedJunctionRightTurn | 184 | 15,288 | 0.8 |
| OppositeVehicleRunningRedLight | 182 | 14,680 | 0.8 |
| ParkedObstacle | 85 | 18,436 | 1.0 |
| AccidentTwoWays | 83 | 17,047 | 0.9 |
| NonSignalizedJunctionRightTurn | 83 | 8,695 | 0.5 |
| ParkingExit | 82 | 11,463 | 0.6 |
| HazardAtSideLaneTwoWays | 82 | 18,565 | 1.0 |
| NonSignalizedJunctionLeftTurn | 81 | 8,909 | 0.5 |
| ParkedObstacleTwoWays | 81 | 12,773 | 0.7 |
| PriorityAtJunction | 81 | 9,756 | 0.5 |
| PedestrianCrossing | 81 | 15,040 | 0.8 |
| ParkingCutIn | 78 | 13,786 | 0.8 |
| OppositeVehicleTakingPriority | 78 | 8,075 | 0.4 |
| BlockedIntersection | 78 | 11,042 | 0.6 |
| VehicleTurningRoutePedestrian | 78 | 20,724 | 1.2 |
| InvadingTurn | 77 | 8,638 | 0.5 |
| VehicleOpensDoorTwoWays | 77 | 12,451 | 0.7 |
| StaticCutIn | 76 | 8,175 | 0.5 |
| InterurbanActorFlow | 76 | 8,240 | 0.5 |
| HardBreakRoute | 75 | 13,504 | 0.8 |
| InterurbanAdvancedActorFlow | 75 | 7,923 | 0.4 |
| HighwayExit | 74 | 11,628 | 0.6 |
| ConstructionObstacle | 73 | 11,031 | 0.6 |
| YieldToEmergencyVehicle | 71 | 11,246 | 0.6 |
| ConstructionObstacleTwoWays | 71 | 13,508 | 0.8 |
| ParkingCrossingPedestrian | 71 | 12,345 | 0.7 |
| MergerIntoSlowTraffic | 69 | 11,952 | 0.7 |
| EnterActorFlow | 69 | 7,966 | 0.4 |
| Accident | 68 | 9,709 | 0.5 |
| MergerIntoSlowTrafficV2 | 67 | 13,591 | 0.8 |
| HazardAtSideLane | 65 | 17,021 | 0.9 |
| HighwayCutIn | 61 | 12,439 | 0.7 |
| CrossingBicycleFlow | 44 | 5,524 | 0.3 |
| EnterActorFlowV2 | 26 | 3,889 | 0.2 |

</details>

<details><summary>Weather (22 presets)</summary>

| weather | routes | frames |
|---|---:|---:|
| ClearNoon | 212 | 30,494 |
| SoftRainSunset | 211 | 31,641 |
| Default | 208 | 31,665 |
| WetCloudyNoon | 206 | 31,978 |
| SoftRainNoon | 203 | 32,406 |
| WetNoon | 199 | 30,164 |
| CloudyNight | 198 | 28,364 |
| SoftRainNight | 191 | 31,424 |
| MidRainSunset | 191 | 29,658 |
| WetCloudySunset | 186 | 25,679 |
| MidRainyNight | 185 | 29,094 |
| WetNight | 182 | 26,775 |
| HardRainNight | 180 | 27,414 |
| ClearSunset | 180 | 23,933 |
| CloudySunset | 178 | 29,295 |
| ClearNight | 178 | 25,938 |
| CloudyNoon | 178 | 27,035 |
| MidRainyNoon | 176 | 27,639 |
| HardRainNoon | 173 | 23,633 |
| WetCloudyNight | 167 | 24,616 |
| DustStorm | 163 | 22,453 |
| HardRainSunset | 155 | 22,273 |

</details>

## Limitations

- Only successful expert episodes: there are no failures, near misses outside the scenarios, or recovery from
  off-route states.
- One ego vehicle (lincoln mkz 2020) and one rig placement; the R1 rig is mounted on this car, not on PCCR's original
  vehicle (see `meta/rig_r1_normal.json` for the exact positions).
- Labels come from the simulator, not from annotators: the boxes are CARLA's actor bounding boxes.

## Acknowledgements

[CARLA](https://carla.org), [Bench2Drive](https://github.com/Thinklab-SJTU/Bench2Drive),
[carla_garage / PDM-Lite](https://github.com/autonomousvision/carla_garage), [BEVAD / bevad-sim](https://github.com/dmholtz/bevad),
[PCCR camera rigs](https://github.com/BaderTim/plentiful-carla-camera-rigs).

	---
	pretty_name: PDM-Lite R1_Normal
	tags:
	- autonomous-driving
	- carla
	- bench2drive
	- end-to-end-driving
	- multi-camera
	- lidar
	size_categories:
	- 100K<n<1M
	configs:
	- config_name: preview
	default: true
	data_files:
	- split: train
	path: preview/*.parquet
	---

	# PDM-Lite R1_Normal

	Expert driving in CARLA 0.9.15 on the full route set of carla_garage's PDM-Lite data (CARLA Leaderboard 2.0
	scenarios, the scenario set Bench2Drive also uses), driven by the rule-based PDM-Lite expert and recorded with **two
	camera rigs at once: BEVAD's six nuScenes-style cameras and the eight-camera PCCR R1** rig, plus a 32-channel
	LiDAR and the simulator's labels at 5 Hz.

	Collection in progress (2026-10-30): 4467 of 5545 routes attempted, 4100 successful routes uploaded so far; new routes are added as they finish. The numbers below are those of 2026-10-30.

	![Front cameras of six routes, real time](samples/front_cameras.gif)

	\| \| \|
	\|---\|---\|
	\| Routes (successful episodes) \| 4,100 \|
	\| Frames (5 Hz) \| 613,571 (34.1 h of driving) \|
	\| Images \| 8,589,994 (14 per frame: 6 BEVAD + 8 R1), JPEG 800 x 448 \|
	\| Size \| 383 GB in 4,100 tar files \|
	\| Scenario types \| 38 scenario types + routes without a scenario (`noScenarios`) \|
	\| Towns \| Town01, Town02, Town03, Town04, Town05, Town10HD, Town12, Town13 \|
	\| Weather \| 22 CARLA presets; 48% of the routes in night mode (vehicle lights on, carla_garage's rule: low sun, heavy cloud or fog) \|
	\| Ego vehicle \| `vehicle.lincoln.mkz_2020` \|

	The Dataset Viewer above shows the `preview` table: one key frame of up to four routes per scenario, with the front
	camera, both surround views and the expert's control. `index.csv` lists every uploaded route (the viewer reads one
	format per repository, so it is not a viewer table).

	## Samples

	All six cameras of BEVAD (`rgb`) and all eight of R1 (`rgb_r1`) at the same instant; in the middle of the R1 view the
	LiDAR (grey) and the world-state boxes in a bird's-eye view, forward up (white: ego, blue: vehicles, orange:
	pedestrians, aqua: bicycles and motorcycles, grey outline: static objects; rings every 10 m).

	DynamicObjectCrossing · Town05 · WetCloudyNoon (`data/lb1_split/DynamicObjectCrossing/Town05_Scenario3_44.tar`, frame 203)

	![BEVAD six cameras](samples/DynamicObjectCrossing_Town05_WetCloudyNoon_bevad6.jpg)

	![R1 eight cameras with LiDAR BEV](samples/DynamicObjectCrossing_Town05_WetCloudyNoon_r1_8cam_bev.jpg)

	More scenes (BEVAD cameras and the bird's-eye view, ±40 m):

	\| BEVAD cameras \| LiDAR + boxes \|
	\|---\|---\|
	\| ![SignalizedJunctionLeftTurn](samples/SignalizedJunctionLeftTurn_Town12_HardRainNight_bevad6.jpg) \| ![SignalizedJunctionLeftTurn BEV](samples/SignalizedJunctionLeftTurn_Town12_HardRainNight_bev.png) \|
	\| SignalizedJunctionLeftTurn · Town12 · HardRainNight · frame 73 \| `data/50x38_Town12/SignalizedJunctionLeftTurn/1002_0.tar` \|
	\| ![ConstructionObstacle](samples/ConstructionObstacle_Town12_MidRainyNoon_bevad6.jpg) \| ![ConstructionObstacle BEV](samples/ConstructionObstacle_Town12_MidRainyNoon_bev.png) \|
	\| ConstructionObstacle · Town12 · MidRainyNoon · frame 58 \| `data/50x38_Town12/ConstructionObstacle/2266_0.tar` \|
	\| ![HighwayCutIn](samples/HighwayCutIn_Town13_ClearSunset_bevad6.jpg) \| ![HighwayCutIn BEV](samples/HighwayCutIn_Town13_ClearSunset_bev.png) \|
	\| HighwayCutIn · Town13 · ClearSunset · frame 40 \| `data/50x36_Town13/HighwayCutIn/1283_16.tar` \|

	The `samples/` folder has the same views for all six scenes.

	## Sensors

	Positions in CARLA's vehicle frame of the actor (x forward, y right, z up; metres, degrees). Every camera is
	rendered at the listed resolution and stored at 800 x 448, JPEG quality 75.

	\| camera \| folder \| x, y, z \| yaw \| pitch \| FoV \| rendered \|
	\|---\|---:\|---:\|---:\|---:\|---:\|---:\|
	\| `CAM_FRONT` \| `rgb` \| 0.60, 0.00, 1.60 \| 0 \| 5 \| 90 \| 1600 x 900 \|
	\| `CAM_FRONT_LEFT` \| `rgb` \| 0.27, -0.55, 1.60 \| -55 \| 0 \| 90 \| 1600 x 900 \|
	\| `CAM_FRONT_RIGHT` \| `rgb` \| 0.27, 0.55, 1.60 \| 55 \| 0 \| 90 \| 1600 x 900 \|
	\| `CAM_BACK` \| `rgb` \| -2.00, 0.00, 1.60 \| 180 \| -5 \| 90 \| 1600 x 900 \|
	\| `CAM_BACK_LEFT` \| `rgb` \| -0.32, -0.55, 1.60 \| -110 \| -5 \| 90 \| 1600 x 900 \|
	\| `CAM_BACK_RIGHT` \| `rgb` \| -0.32, 0.55, 1.60 \| 110 \| -5 \| 90 \| 1600 x 900 \|
	\| `CAM_BUMPER_FRONT` \| `rgb_r1` \| 2.36, 0.00, 0.68 \| 0 \| 0 \| 65 \| 1280 x 720 \|
	\| `CAM_FENDER_FRONT_RIGHT` \| `rgb_r1` \| 1.09, 0.99, 0.72 \| 41 \| 0 \| 65 \| 1280 x 720 \|
	\| `CAM_SIDE_RIGHT` \| `rgb_r1` \| -0.22, 0.74, 1.40 \| 90 \| 0 \| 65 \| 1280 x 720 \|
	\| `CAM_FENDER_BACK_RIGHT` \| `rgb_r1` \| -1.57, 0.99, 0.84 \| 135 \| 0 \| 65 \| 1280 x 720 \|
	\| `CAM_BUMPER_BACK` \| `rgb_r1` \| -2.44, 0.00, 0.64 \| 180 \| 0 \| 65 \| 1280 x 720 \|
	\| `CAM_FENDER_BACK_LEFT` \| `rgb_r1` \| -1.63, -0.99, 0.86 \| -135 \| 0 \| 65 \| 1280 x 720 \|
	\| `CAM_SIDE_LEFT` \| `rgb_r1` \| -0.20, -0.78, 1.40 \| -90 \| 0 \| 65 \| 1280 x 720 \|
	\| `CAM_FENDER_FRONT_LEFT` \| `rgb_r1` \| 1.23, -0.95, 0.78 \| -43 \| 0 \| 65 \| 1280 x 720 \|

	LiDAR `LIDAR_TOP` (`point_cloud/`, LAZ): 32 channels, range 100 m, vertical FoV
	-30° to +10°, at (-0.46, 0.0, 1.86). IMU and GNSS
	are part of the episode metadata. The full rig with every attribute is `meta/rig_r1_normal.json`.

	## What a frame contains

	Each route is one episode in bevad-sim's `EpisodeIo` format: `ep_data.zstd` (zstandard-compressed pickle of a
	`CoreContainer`) next to the sensor folders. Arrays have a leading batch axis of 1, then time (5 Hz), then the slot.

	\| field \| content \|
	\|---\|---\|
	\| `world_state` \| every tracked object incl. the ego: 4x4 pose (world), size, category (car, truck, bus, ambulance, pedestrian, bicycle, motorcycle, construction, roadway item), attribute (parking, driving, emergency, open door, ...), track id, visibility, validity \|
	\| `tce` \| traffic lights and signs: pose, size, control box, state (red / yellow / green / off, sign type) \|
	\| `odometry` \| GNSS, compass, angular velocity, acceleration \|
	\| `action.simulator_control` \| the expert's throttle, steer, brake per frame \|
	\| `routing_information` \| target route and route commands, navigation goal per frame \|
	\| `rgb`, `rgb_r1`, `point_cloud` \| per camera / LiDAR: file per frame (named by simulator frame id), timestamps, intrinsics, extrinsics (ego -> sensor) \|
	\| `episode_meta` \| scenario type, town, route file, weather parameters, ego size \|

	Labels and point clouds are in the ego frame of bevad-sim: x forward, y left, z up, origin at the centre of the ego's
	bounding box (0.75 m above the ground for this car). Static map data is not stored; the towns are CARLA's.

	## Layout

	```
	data/<set>/<scenario>/<route>.tar one route; unpacks to <set>/<scenario>/<route>/
	summary.json seed, frames, route score, sizes
	collect.log the collection log of that route
	<route>-<id>/ep_data.zstd labels (see above)
	<route>-<id>/rgb/CAM_*/<frame>.jpg BEVAD cameras
	<route>-<id>/rgb_r1/CAM_*/<frame>.jpg R1 cameras
	<route>-<id>/point_cloud/LIDAR_TOP/<frame>.laz
	index.csv uploaded routes: order, set, scenario, town, weather, night, seed, frames, score, size, tar
	meta/rig_r1_normal.json sensor rig
	meta/routes_index.csv all 5545 route files with seed and weather
	preview/, samples/ the viewer table and the figures of this card
	```

	## Loading

	```python
	from huggingface_hub import hf_hub_download
	import tarfile

	p = hf_hub_download("shdragron/pdm-lite-train-R1_Normal", "data/lb1_split/DynamicObjectCrossing/Town05_Scenario3_44.tar", repo_type="dataset")
	tarfile.open(p).extractall("pdm_lite")
	```

	```python
	# pip install -e <github.com/dmholtz/bevad>/bevad-sim (bevad-sim reads the episode format)
	import glob
	from bevad_sim.data_interface.episode_io import EpisodeIo

	ep_dir = glob.glob("pdm_lite/lb1_split/DynamicObjectCrossing/Town05_Scenario3_44/*/")[0]
	ep = EpisodeIo.read_episode(ep_dir, load_payload=False) # labels only; images and LiDAR stay on disk
	ws = ep.world_state
	print(ws.transform.shape) # (1, frames, objects, 4, 4)
	cam = ep.rgb.sensor_names.index("CAM_FRONT")
	frame_id = int(ep.rgb.frame_ids[0, 0, cam]) # -> {ep_dir}rgb/CAM_FRONT/<frame_id>.jpg
	```

	`ep_data.zstd` is a pickle: open it only from this repository (or other sources you trust).

	## Collection

	- Expert: PDM-Lite (carla_garage, `team_code/autopilot.py`) through BEVAD's bevad-sim port, with carla_garage's
	red-light stop distance (6 m) and vehicle lights in night mode restored, so it drives as carla_garage's original.
	- Routes: every route file of carla_garage's PDM-Lite data set (`50x38_Town12`, `50x36_Town13`, `lb1_split`;
	5545 files, one route each), in carla_garage's shuffled order (seed 42); the traffic-manager seed counts up by
	one per route.
	- Weather: one of the first 22 CARLA presets per route, as carla_garage's `shuffle_weather`, fixed by the route seed.
	- Episodes: simulator at 20 Hz, recorded every 4th tick (5 Hz). A route that fails (scenario could not be spawned,
	simulator crash, 90 min time-out) gets one more try on a fresh server; only routes whose episode succeeded are here.

	## Distribution

	<details><summary>Sets and towns</summary>

	\| set \| town \| routes \| frames \| hours \|
	\|---\|---:\|---:\|---:\|---:\|
	\| 50x36_Town13 \| Town13 \| 1376 \| 221,095 \| 12.3 \|
	\| 50x38_Town12 \| Town12 \| 1428 \| 218,363 \| 12.1 \|
	\| lb1_split \| Town01 \| 90 \| 10,333 \| 0.6 \|
	\| lb1_split \| Town02 \| 58 \| 6,606 \| 0.4 \|
	\| lb1_split \| Town03 \| 213 \| 30,771 \| 1.7 \|
	\| lb1_split \| Town04 \| 399 \| 45,131 \| 2.5 \|
	\| lb1_split \| Town05 \| 426 \| 63,029 \| 3.5 \|
	\| lb1_split \| Town10HD \| 110 \| 18,243 \| 1.0 \|

	</details>

	<details><summary>Scenarios (39)</summary>

	\| scenario \| routes \| frames \| hours \|
	\|---\|---:\|---:\|---:\|
	\| VehicleTurningRoute \| 439 \| 81,148 \| 4.5 \|
	\| noScenarios \| 290 \| 28,416 \| 1.6 \|
	\| SignalizedJunctionLeftTurn \| 269 \| 21,195 \| 1.2 \|
	\| ControlLoss \| 209 \| 39,031 \| 2.2 \|
	\| DynamicObjectCrossing \| 191 \| 38,722 \| 2.2 \|
	\| SignalizedJunctionRightTurn \| 184 \| 15,288 \| 0.8 \|
	\| OppositeVehicleRunningRedLight \| 182 \| 14,680 \| 0.8 \|
	\| ParkedObstacle \| 85 \| 18,436 \| 1.0 \|
	\| AccidentTwoWays \| 83 \| 17,047 \| 0.9 \|
	\| NonSignalizedJunctionRightTurn \| 83 \| 8,695 \| 0.5 \|
	\| ParkingExit \| 82 \| 11,463 \| 0.6 \|
	\| HazardAtSideLaneTwoWays \| 82 \| 18,565 \| 1.0 \|
	\| NonSignalizedJunctionLeftTurn \| 81 \| 8,909 \| 0.5 \|
	\| ParkedObstacleTwoWays \| 81 \| 12,773 \| 0.7 \|
	\| PriorityAtJunction \| 81 \| 9,756 \| 0.5 \|
	\| PedestrianCrossing \| 81 \| 15,040 \| 0.8 \|
	\| ParkingCutIn \| 78 \| 13,786 \| 0.8 \|
	\| OppositeVehicleTakingPriority \| 78 \| 8,075 \| 0.4 \|
	\| BlockedIntersection \| 78 \| 11,042 \| 0.6 \|
	\| VehicleTurningRoutePedestrian \| 78 \| 20,724 \| 1.2 \|
	\| InvadingTurn \| 77 \| 8,638 \| 0.5 \|
	\| VehicleOpensDoorTwoWays \| 77 \| 12,451 \| 0.7 \|
	\| StaticCutIn \| 76 \| 8,175 \| 0.5 \|
	\| InterurbanActorFlow \| 76 \| 8,240 \| 0.5 \|
	\| HardBreakRoute \| 75 \| 13,504 \| 0.8 \|
	\| InterurbanAdvancedActorFlow \| 75 \| 7,923 \| 0.4 \|
	\| HighwayExit \| 74 \| 11,628 \| 0.6 \|
	\| ConstructionObstacle \| 73 \| 11,031 \| 0.6 \|
	\| YieldToEmergencyVehicle \| 71 \| 11,246 \| 0.6 \|
	\| ConstructionObstacleTwoWays \| 71 \| 13,508 \| 0.8 \|
	\| ParkingCrossingPedestrian \| 71 \| 12,345 \| 0.7 \|
	\| MergerIntoSlowTraffic \| 69 \| 11,952 \| 0.7 \|
	\| EnterActorFlow \| 69 \| 7,966 \| 0.4 \|
	\| Accident \| 68 \| 9,709 \| 0.5 \|
	\| MergerIntoSlowTrafficV2 \| 67 \| 13,591 \| 0.8 \|
	\| HazardAtSideLane \| 65 \| 17,021 \| 0.9 \|
	\| HighwayCutIn \| 61 \| 12,439 \| 0.7 \|
	\| CrossingBicycleFlow \| 44 \| 5,524 \| 0.3 \|
	\| EnterActorFlowV2 \| 26 \| 3,889 \| 0.2 \|

	</details>

	<details><summary>Weather (22 presets)</summary>

	\| weather \| routes \| frames \|
	\|---\|---:\|---:\|
	\| ClearNoon \| 212 \| 30,494 \|
	\| SoftRainSunset \| 211 \| 31,641 \|
	\| Default \| 208 \| 31,665 \|
	\| WetCloudyNoon \| 206 \| 31,978 \|
	\| SoftRainNoon \| 203 \| 32,406 \|
	\| WetNoon \| 199 \| 30,164 \|
	\| CloudyNight \| 198 \| 28,364 \|
	\| SoftRainNight \| 191 \| 31,424 \|
	\| MidRainSunset \| 191 \| 29,658 \|
	\| WetCloudySunset \| 186 \| 25,679 \|
	\| MidRainyNight \| 185 \| 29,094 \|
	\| WetNight \| 182 \| 26,775 \|
	\| HardRainNight \| 180 \| 27,414 \|
	\| ClearSunset \| 180 \| 23,933 \|
	\| CloudySunset \| 178 \| 29,295 \|
	\| ClearNight \| 178 \| 25,938 \|
	\| CloudyNoon \| 178 \| 27,035 \|
	\| MidRainyNoon \| 176 \| 27,639 \|
	\| HardRainNoon \| 173 \| 23,633 \|
	\| WetCloudyNight \| 167 \| 24,616 \|
	\| DustStorm \| 163 \| 22,453 \|
	\| HardRainSunset \| 155 \| 22,273 \|

	</details>

	## Limitations

	- Only successful expert episodes: there are no failures, near misses outside the scenarios, or recovery from
	off-route states.
	- One ego vehicle (lincoln mkz 2020) and one rig placement; the R1 rig is mounted on this car, not on PCCR's original
	vehicle (see `meta/rig_r1_normal.json` for the exact positions).
	- Labels come from the simulator, not from annotators: the boxes are CARLA's actor bounding boxes.

	## Acknowledgements

	[CARLA](https://carla.org), [Bench2Drive](https://github.com/Thinklab-SJTU/Bench2Drive),
	[carla_garage / PDM-Lite](https://github.com/autonomousvision/carla_garage), [BEVAD / bevad-sim](https://github.com/dmholtz/bevad),
	[PCCR camera rigs](https://github.com/BaderTim/plentiful-carla-camera-rigs).