# Sensor And Simulation Configuration

This document records the sensor, simulator, traffic, weather, and synchronization settings used for the EgoSpatial-CL v1.0.0 dataset release. Values are derived from the current generation scripts, release manifests, and episode-level `meta.json` files.

## Release Summary

```text
Dataset: EgoSpatial-CL
Version: v1.0.0
Simulator: CARLA 0.9.15
Execution mode: Docker, headless/offscreen rendering
Synchronous mode: enabled
Fixed delta seconds: 0.05
Capture stride: 10 simulator ticks
Saved samples per episode: 1,000
Nominal saved-sample interval: 0.5 simulation seconds
Episodes: 490
Synchronized samples: 490,000
```

The dataset records rendered camera data; therefore CARLA `no_rendering_mode_capture` is `false` for all release episodes.

## Generation Runtime

The v1.0.0 generation audit records the following container image digest:

```text
sha256:1b5417ab03a91d2bad581d41fabeb91a8f3836130d7a112bcc8b4d2dac7dcfcd
```

Episode metadata in the frozen dataset records:

```text
host = 127.0.0.1
port = 2000
synchronous_mode = true
fixed_delta_seconds = 0.05
no_rendering_mode_capture = false
```

These host/port values describe the local collection setup, not a requirement for using the released dataset.

## Temporal Synchronization

All sensors were configured with `sensor_tick = 0.05`, matching the fixed simulator step. A saved sample is produced after advancing the simulation by the capture stride:

```text
fixed_delta_seconds = 0.05 seconds
sensor_tick = 0.05 seconds
capture_stride = 10 ticks
saved-sample interval = 0.05 x 10 = 0.5 simulation seconds
```

For each saved sample, `state.csv`, `index.csv`, RGB, semantic segmentation, and LiDAR share the same CARLA frame identifier.

## Sensor Suite

| Sensor / signal | CARLA actor or source | Stored output | Release status |
|---|---|---|---|
| Front RGB camera | `sensor.camera.rgb` | `images/<frame>.png` | enabled |
| Semantic-segmentation camera | `sensor.camera.semantic_segmentation` | `labels/<frame>.png` | enabled |
| LiDAR | `sensor.lidar.ray_cast` | `lidar/<frame>.npy` | enabled |
| IMU | `sensor.other.imu` | columns in `state.csv` | enabled |
| GNSS | `sensor.other.gnss` | columns in `state.csv` | enabled |
| Ego pose/kinematics/control | ego vehicle actor | columns in `state.csv` | enabled |

The release does not include dense depth, optical flow, 2D bounding boxes, 3D bounding boxes, tracking IDs, multi-view cameras, radar, or instance segmentation.

## Front RGB Camera

The front RGB camera is attached to the ego vehicle.

```text
CARLA blueprint: sensor.camera.rgb
image width: 800 pixels
image height: 600 pixels
field of view: 90 degrees
sensor_tick: 0.05 seconds
postprocess effects: disabled when supported by the CARLA blueprint
```

Transform relative to the ego vehicle:

| Component | Value | Unit |
|---|---:|---|
| `location.x` | 1.5 | meters |
| `location.y` | 0.0 | meters |
| `location.z` | 1.7 | meters |
| `rotation.pitch` | -5.0 | degrees |
| `rotation.yaw` | 0.0 | degrees |
| `rotation.roll` | 0.0 | degrees |

Saved format:

```text
directory: images/
filename pattern: %06d.png using the CARLA frame id
color conversion: CARLA raw BGRA -> RGB
file format: PNG
```

## Semantic-Segmentation Camera

The semantic-segmentation camera is aligned with the front RGB camera.

```text
CARLA blueprint: sensor.camera.semantic_segmentation
image width: 800 pixels
image height: 600 pixels
field of view: 90 degrees
sensor_tick: 0.05 seconds
transform: same as front RGB camera
```

Saved format:

```text
directory: labels/
filename pattern: %06d.png using the CARLA frame id
conversion before saving: carla.ColorConverter.CityScapesPalette
file format: PNG
```

Important interpretation note: the release stores color-encoded semantic PNGs after `CityScapesPalette` conversion. They are not single-channel integer class-index rasters. Users requiring class-index masks should map palette colors back to semantic classes using the CARLA semantic-label definitions for CARLA 0.9.15.

## LiDAR

LiDAR point clouds are attached to the ego vehicle and saved once per synchronized sample.

```text
CARLA blueprint: sensor.lidar.ray_cast
sensor_tick: 0.05 seconds
rotation_frequency: 20 Hz
points_per_second: 100000
channels: 32
range: 50.0 meters
upper_fov: 10.0 degrees
lower_fov: -30.0 degrees
```

Transform relative to the ego vehicle:

| Component | Value | Unit |
|---|---:|---|
| `location.x` | 0.0 | meters |
| `location.y` | 0.0 | meters |
| `location.z` | 2.0 | meters |
| `rotation.pitch` | 0.0 | degrees |
| `rotation.yaw` | 0.0 | degrees |
| `rotation.roll` | 0.0 | degrees |

Saved format:

```text
directory: lidar/
filename pattern: %06d.npy using the CARLA frame id
NumPy dtype: float32
NumPy shape: (N, 4)
columns: x, y, z, intensity
allow_pickle: false
```

`N` can vary across frames because it depends on the simulated scene geometry and valid LiDAR returns. Point coordinates are stored in the LiDAR sensor frame.

## IMU

The IMU is attached to the ego vehicle at the actor origin:

```text
CARLA blueprint: sensor.other.imu
sensor_tick: 0.05 seconds
relative transform: location (0, 0, 0), rotation (0, 0, 0)
stored in: state.csv
```

Stored IMU fields:

| Field group | Columns | Unit |
|---|---|---|
| Linear acceleration | `imu_accel_x`, `imu_accel_y`, `imu_accel_z` | m/s^2 |
| Angular velocity | `imu_gyro_x`, `imu_gyro_y`, `imu_gyro_z` | rad/s |
| Compass | `imu_compass` | radians |

The IMU acceleration follows CARLA IMU behavior and includes gravity in the sensor-frame measurement.

## GNSS

The GNSS sensor is attached to the ego vehicle at the actor origin:

```text
CARLA blueprint: sensor.other.gnss
sensor_tick: 0.05 seconds
relative transform: location (0, 0, 0), rotation (0, 0, 0)
stored in: state.csv
```

Stored GNSS fields:

| Field | Unit | Description |
|---|---|---|
| `gnss_lat` | degrees | Latitude reported by CARLA GNSS. |
| `gnss_lon` | degrees | Longitude reported by CARLA GNSS. |
| `gnss_alt` | meters | Altitude reported by CARLA GNSS. |

## Ego Vehicle

All release episodes use the same ego vehicle blueprint:

```text
ego blueprint: vehicle.tesla.model3
autopilot: true
Traffic Manager port: 8000
```

Ego Traffic Manager configuration recorded across all release episodes:

| Setting | Value | Unit / type |
|---|---:|---|
| `distance_to_leading` | 3.5 | meters |
| `speed_diff_pct` | 60.0 | percent |
| `ignore_lights_pct` | 0.0 | percent |
| `ignore_signs_pct` | 0.0 | percent |
| `ignore_walkers_pct` | 0.0 | percent |
| `auto_lane_change` | false | boolean |
| `hybrid_physics` | false | boolean |
| `hybrid_radius` | 70.0 | meters |

The ego vehicle state and control values are recorded in `state.csv`.

## NPC Traffic And Walkers

NPC settings depend on the compact domain. Requested NPC counts are:

| Domain | Requested vehicles | Requested walkers |
|---|---:|---:|
| `clear__NPC00_none` | 0 | 0 |
| `night__NPC00_none` | 0 | 0 |
| `fog__NPC00_none` | 0 | 0 |
| `hardrain__NPC00_none` | 0 | 0 |
| `clear__NPC11_veh_high` | 50 | 0 |
| `hardrain__NPC11_veh_high` | 50 | 0 |
| `clear__NPC31_both_high` | 50 | 200 |

Common NPC configuration:

```text
npc_vehicle_filter = vehicle.*
npc_min_dist_to_ego = 15.0 meters
tm_npc_speed_diff_pct = 20.0
tm_npc_auto_lane_change = false
```

Walker settings for pedestrian domains:

```text
pedestrians_cross_factor = 0.6
walker_speed_min = 1.2 m/s
walker_speed_max = 2.0 m/s
```

For no-pedestrian domains, `pedestrians_cross_factor` is recorded as the collector default `0.1`, and walker speeds are recorded as defaults even though no walkers are requested.

`vehicles_spawned` and `walkers_spawned` can be lower than requested counts because spawning depends on available map spawn points, minimum distance constraints, and valid navigation locations.

## Weather And Domain Parameters

The public compact domain names use release-level labels (`clear`, `night`, `fog`, `hardrain`, and NPC variants). The raw `meta.json.weather` field records the collector preset name. Some release domains, especially `night` and `fog`, are defined by weather-parameter overrides in addition to the preset.

Representative recorded weather parameters by compact domain:

| Domain | `meta.json.weather` | `cloudiness` | `precipitation` | `precipitation_deposits` | `wind_intensity` | `fog_density` | `fog_distance` | `fog_falloff` | `sun_altitude_angle` |
|---|---|---:|---:|---:|---:|---:|---:|---:|---:|
| `clear__NPC00_none` | `clear` | 5.0 | 0.0 | 0.0 | 10.0 | 2.0 | 0.75 | 0.1 | 45.0 |
| `night__NPC00_none` | `clear` | 25.0 | 0.0 | 0.0 | 10.0 | 2.0 | 0.75 | 0.1 | -10.0 |
| `fog__NPC00_none` | `cloudy` | 60.0 | 0.0 | 0.0 | 10.0 | 50.0 | 30.0 | 1.0 | 25.0 |
| `hardrain__NPC00_none` | `hardrain` | 100.0 | 100.0 | 90.0 | 100.0 | 7.0 | 0.75 | 0.1 | 45.0 |
| `clear__NPC11_veh_high` | `clear` | 5.0 | 0.0 | 0.0 | 10.0 | 2.0 | 0.75 | 0.1 | 45.0 |
| `hardrain__NPC11_veh_high` | `hardrain` | 100.0 | 100.0 | 90.0 | 100.0 | 7.0 | 0.75 | 0.1 | 45.0 |
| `clear__NPC31_both_high` | `clear` | 5.0 | 0.0 | 0.0 | 10.0 | 2.0 | 0.75 | 0.1 | 45.0 |

Additional recorded weather fields include:

```text
wetness
sun_azimuth_angle
scattering_intensity
mie_scattering_scale
rayleigh_scattering_scale
dust_storm
```

These fields are documented in `docs/data_dictionary.md`.

## Towns And Spatial Slots

The release includes seven towns:

```text
Town01_Opt
Town02_Opt
Town04_Opt
Town05_Opt
Town06
Town07
Town10HD_Opt
```

Each town uses 10 frozen spawn slots reused across all compact domains:

```text
8 train slots
1 validation slot
1 test slot
```

The exact spawn indices are stored in `spatial_splits.json` and episode-level records in `benchmark_manifest.json`.

Excluded from release v1.0.0:

```text
Town03
Town03_Opt
Town12
```

Town12 was excluded because the local CARLA 0.9.15 headless Docker setup was unstable when sensors were attached to the ego vehicle. The other excluded towns are outside the frozen v1.0.0 release scope.

## Collection Stability Parameters

The final collection workflow used conservative synchronization and shutdown settings:

```text
warmup_ticks = 20
post_load_sleep = 2.0 seconds
map_load_warmup_ticks = 40
sensor_timeout = 20.0 seconds
rpc_timeout = 30.0 seconds
shutdown_sleep = 4.0 seconds
park_server = true
park_fixed_dt = 0.05 seconds
```

These settings are provenance for dataset generation. Users loading the released data do not need to reproduce the live simulator runtime unless regenerating the dataset.

## Interpretation Notes

- `state.csv` actor pose, velocity, acceleration, angular velocity, control, IMU, and GNSS values are synchronized to saved sample frames.
- RGB and semantic-segmentation images are both 800 x 600 PNG files aligned by simulator frame.
- Semantic-segmentation PNGs are palette-color images, not integer class-index masks.
- LiDAR files contain variable-length point clouds with shape `(N, 4)` and dtype `float32`.
- The dataset is simulated; CARLA-specific coordinate, rendering, semantic-label, vehicle-control, and Traffic Manager behavior should be acknowledged in downstream studies.