--- pretty_name: Scene2Wave license: other license_name: scene2wave-layered-license-notice license_link: https://huggingface.co/datasets/mfzheng/Scene2Wave/blob/main/LICENSE task_categories: - feature-extraction size_categories: - n<1K tags: - wireless - 6g - multimodal - channel-impulse-response - ray-tracing - autonomous-driving - lidar - radar - carla - sionna --- # Scene2Wave Scene2Wave is a synchronized multimodal wireless-channel dataset generated with CARLA and Sionna RT. Version 1.0.1 contains 100 formally validated samples spanning four CARLA Towns, five motion states, four radio profiles, multi-view cameras, Birdview, LiDAR, Radar, IMU, GNSS, poses, path-level CIR, and CSI. The Scene2Wave Project Content rightsholder is **Pengcheng Laboratory**. Based on the current generation configuration, the dataset is known to use official CARLA assets and the official Sionna software stack; no separately sourced scene assets have been identified. ## Authors Mengfan Zheng, Liwen Jing, Tingting Yang, Li Sun, Yuxuan Shi, Ping Zhang, Leiyang Xu, and Jianjun Chen. ## Dataset summary | Property | Value | | --- | --- | | Formal samples | 100 | | Towns | Town01, Town02, Town03, Town15; 25 samples each | | Motion states | static, 10, 20, 40, 60 km/h; 20 samples each | | Profiles | core 40, multipath 20, scattering mild 20, scattering medium 20 | | Carrier frequency | 3.7 GHz | | Bandwidth | 15.36 MHz | | Subcarriers | 512 at 30 kHz spacing | | CSI sampling rate | 2 kHz | | Nominal sample duration | 1 second | | CARLA | 0.9.16 | | Sionna | 0.19.2 | ## Propagation profiles within each motion state Each motion state (`static`, `10kmh`, `20kmh`, `40kmh`, or `60kmh`) contains 20 samples: five distinct CARLA runs from each of the four Towns. Within every Town × motion-state bucket, those five runs are allocated as two `core` and one each of `multipath`, `scattering_mild`, and `scattering_medium`. Thus each motion state contains 8/4/4/4 samples respectively, and the complete release contains 40/20/20/20. All four profiles enable LOS, specular reflection, diffraction, and edge diffraction. `num_samples` below is the Sionna ray-launching budget per source, not the number of dataset examples. `max_depth` is the maximum interaction depth used by the path solver. | Profile | Per Town × state | Per state | `num_samples` | `max_depth` | Scattering | Material profile / scale | `scat_keep_prob` | | --- | ---: | ---: | ---: | ---: | --- | --- | ---: | | `core` | 2 | 8 | 1,000,000 | 2 | disabled | none / inactive | inactive | | `multipath` | 1 | 4 | 2,000,000 | 3 | disabled | none / inactive | inactive | | `scattering_mild` | 1 | 4 | 2,000,000 | 3 | enabled | `urban_37ghz` / 0.30 | 0.0002 | | `scattering_medium` | 1 | 4 | 2,000,000 | 3 | enabled | `urban_37ghz` / 0.60 | 0.0005 | The profiles have the following intended meanings: - **`core`** is the baseline propagation setting. Its shallower interaction depth and smaller ray budget emphasize dominant LOS, specular, and diffracted components while keeping the baseline representative and computationally bounded. - **`multipath`** disables diffuse scattering like `core`, but doubles the ray budget and raises the interaction depth from 2 to 3. It is intended to retain more higher-order reflected/diffracted paths and represent a denser non-scattering multipath condition. - **`scattering_mild`** uses the multipath solver budget and enables the `urban_37ghz` material-scattering model at scale 0.30. It introduces a moderate diffuse component for robustness and material-scattering sensitivity studies. - **`scattering_medium`** raises the same material-scattering scale to 0.60 and increases scattering-path retention. It represents the more difficult diffuse-scattering condition included in the main training distribution. `material_scattering_scale` is a relative multiplier applied to the configured urban material-scattering profile; it is not a percentage of total received power. Likewise, `scat_keep_prob` controls stochastic retention of candidate scattering paths for tractable simulation and is not the physical probability that a surface scatters. These are independently generated `main_training` samples, not four RF reruns of identical CARLA geometry. Profile comparisons should therefore use stratified or aggregate analysis across Town and motion state; a difference between two individual samples must not be attributed solely to the profile. ## Sensor suite and acquisition configuration The CAV is a CARLA `vehicle.tesla.model3`. The roadside unit (RSU) is mounted 5 m above the sampled road surface. The following table records the formal release configuration; counts and rates are per sample unless stated otherwise. | Platform / modality | Configuration | Nominal output | | --- | --- | --- | | CAV RGB cameras | 4 views: front, rear, right, left; 1280 × 720; 110° horizontal FOV; camera height 2.4 m | 20 Hz, 20 frames/view | | CAV depth cameras | 4 views co-located with the RGB cameras; 1280 × 720; 110° horizontal FOV | 20 Hz, 20 frames/view | | CAV LiDAR | 128 channels; 120 m range; 360° horizontal FOV; −10° to +90° vertical FOV; mounted at 2.5 m | 20 Hz, target 30,000 points/frame | | CAV IMU | acceleration noise std. dev. 0.1 m/s²/axis; gyroscope noise std. dev. 0.002 rad/s/axis; gyro bias 0.001 rad/s/axis; seed 42 | 100 Hz, 100 records | | CAV GNSS | latitude, longitude, and altitude noise std. dev. set to zero | 10 Hz, 10 records | | RSU RGB cameras | 4 cardinal views: east, north, west, south; 1280 × 720; 110° horizontal FOV | 20 Hz, 20 frames/view | | RSU depth cameras | 4 views co-located with the RSU RGB cameras; 1280 × 720; 110° horizontal FOV | 20 Hz, 20 frames/view | | RSU LiDAR | 128 channels; 120 m range; 360° horizontal FOV; −50° to +50° vertical FOV | 20 Hz, target 30,000 points/frame | | RSU Radar | 100 m range; 120° horizontal × 30° vertical FOV; yaw chosen to face the road | 20 Hz, 10,000-point ray budget/frame; stored detections vary by scene | | Birdview RGB | top-down camera at 130 m; 1280 × 1280; 90° FOV; yaw 0° | 20 Hz, 20 frames | The 1-second recorded interval begins after a 0.3-second simulator warm-up. CARLA geometry/pose and CIR/CSI use a 2 kHz clock (2,000 samples), while the physical sensors retain the native rates above. Streams are therefore **time-aligned but not rate-equal**. Use each archive's `carla/alignment_index.json`: camera, LiDAR, Radar, and other sensor records are mapped to the nearest 2 kHz geometry/CSI frame. Do not assume that ordinal file indices from two modalities identify the same timestamp. The LiDAR point counts are generation targets rather than a promise that every serialized cloud has exactly that size. Similarly, the Radar setting is a ray budget; CARLA writes only returned detections, so the number of stored Radar points depends on scene content. Per-frame poses and timestamps are stored with the sensor data, and the full machine-readable release policy is in `metadata/generation_config.yaml`. Every formal sample passed CARLA trajectory and collision qualification before wireless ray tracing. The release contains no candidate samples, visual QA panels, videos, logs, cache keys, or editable simulator assets. ## Packaged layout To keep the Hub repository usable, each sample is distributed as one deterministic, uncompressed tar archive instead of hundreds of thousands of small files: ```text Scene2Wave/ ├── README.md ├── LICENSE ├── ATTRIBUTION.md ├── THIRD_PARTY_NOTICES.md ├── CITATION.cff ├── DATA_PROVENANCE.md ├── dataset_info.json ├── schema.md ├── checksums.sha256 ├── metadata/ │ ├── samples.jsonl │ ├── source_samples.jsonl │ ├── json_schema.json │ ├── generation_config.yaml │ └── raw_dataset_schema.md └── data/main_training////.tar ``` Each tar has `sample_metadata.json`, `carla/`, and `sionna/` at its member root. `metadata/samples.jsonl` adds `archive_path`, `archive_bytes`, and `archive_sha256` to the public sample record. `source_samples.jsonl` preserves the original unpacked-tree index. ## Load one sample without extracting the full dataset ```python import io import json import tarfile from pathlib import Path import numpy as np root = Path("Scene2Wave") records = [ json.loads(line) for line in (root / "metadata/samples.jsonl").read_text().splitlines() if line.strip() ] record = records[0] with tarfile.open(root / record["archive_path"], mode="r:") as archive: member = archive.extractfile("./sample_metadata.json") metadata = json.load(member) npz_name = next( name for name in archive.getnames() if name.startswith("./sionna/") and name.endswith("_paths.npz") ) payload = archive.extractfile(npz_name).read() with np.load(io.BytesIO(payload), allow_pickle=False) as arrays: print(record["sample_id"], arrays.files) print(metadata["radio"]["carrier_frequency_hz"]) ``` For repeated access, extract only the required sample: ```bash mkdir sample tar -xf data/main_training/Town01/10kmh/core/Req_Town01_dynamic_10kmh_t001_rfmain.tar -C sample ``` See `schema.md` for archive handling and `metadata/raw_dataset_schema.md` for the complete sensor, alignment, radar, and wireless field reference. ## Integrity `checksums.sha256` covers every sample archive and all release metadata: ```bash sha256sum --check checksums.sha256 ``` ## Intended uses - multimodal wireless-channel representation learning; - CIR/CSI estimation and prediction; - perception-to-channel feature extraction; - synchronization, fusion, and robustness research; - controlled comparisons across Town, speed, and propagation profile. ## Limitations - This is simulated data and does not reproduce every real sensor, material, traffic, weather, hardware, calibration, or regulatory condition. - Only four CARLA Towns and the documented RF configurations are represented. - Dataset splits must avoid leakage between closely related scenarios when evaluating generalization. - Empty or outage links require explicit handling even though the formal release records valid CIR for all indexed samples. - Users remain responsible for validating conclusions against real-world data. ## License and third-party materials This repository uses a layered license. To the extent Pengcheng Laboratory owns the applicable rights, Scene2Wave Project Content is licensed under CC BY 4.0. CARLA-derived elements and other third-party materials retain their respective terms. Read `LICENSE`, `ATTRIBUTION.md`, and `THIRD_PARTY_NOTICES.md`; the Hugging Face metadata therefore uses `license: other` rather than applying one license to every layer. ## Citation Until a dataset paper or DOI is assigned, cite this release using the metadata in `CITATION.cff` and include the repository URL and version. Preserve the author order listed above.