{ "@context": { "@language": "en", "@vocab": "https://schema.org/", "citeAs": "cr:citeAs", "column": "cr:column", "conformsTo": "dct:conformsTo", "cr": "http://mlcommons.org/croissant/", "rai": "http://mlcommons.org/croissant/RAI/", "data": { "@id": "cr:data", "@type": "@json" }, "dataType": { "@id": "cr:dataType", "@type": "@vocab" }, "dct": "http://purl.org/dc/terms/", "examples": { "@id": "cr:examples", "@type": "@json" }, "extract": "cr:extract", "field": "cr:field", "fileProperty": "cr:fileProperty", "fileObject": "cr:fileObject", "fileSet": "cr:fileSet", "format": "cr:format", "includes": "cr:includes", "isLiveDataset": "cr:isLiveDataset", "jsonPath": "cr:jsonPath", "key": "cr:key", "md5": "cr:md5", "parentField": "cr:parentField", "path": "cr:path", "recordSet": "cr:recordSet", "references": "cr:references", "regex": "cr:regex", "repeated": "cr:repeated", "replace": "cr:replace", "sc": "https://schema.org/", "separator": "cr:separator", "source": "cr:source", "subField": "cr:subField", "transform": "cr:transform" }, "@type": "sc:Dataset", "name": "rad-r", "description": "Rad-R: A Real-World Raw-ADC Dataset and Benchmark for mmWave Automotive Radar Robustness. The first publicly released radar dataset combining raw ADC captures from a 4-chip TI MMWCAS-RF-EVM cascade radar (12 Tx x 16 Rx, 77 GHz) with physically induced hardware fault annotations across 4 fault types and 2 severity levels each, plus a healthy baseline. Synchronized with BNO055 IMU, DHT22 temperature, three GPS streams, and an Intel RealSense D435 camera (two co-recorded streams). Rad-R contains 9 capture runs (5 min each, 10 fps) totalling ~27 k radar frames.", "conformsTo": "http://mlcommons.org/croissant/1.0", "url": "https://huggingface.co/datasets/radr-anon-2026/radr", "version": "1.0.0", "datePublished": "2026-05-07", "license": "https://creativecommons.org/licenses/by/4.0/", "citeAs": "@inproceedings{radr2027, title={Rad-R: A Real-World Raw-ADC Dataset and Benchmark for mmWave Automotive Radar Robustness}, author={Anonymous}, booktitle={WACV Evaluations and Datasets Track}, year={2027}}", "keywords": [ "radar", "mmwave", "raw-adc", "fault diagnosis", "automotive radar", "sensor fusion", "benchmark" ], "creator": { "@type": "Organization", "name": "Anonymous (WACV 2027 submission)" }, "rai:dataCollection": "Captured indoor and outdoor with a vehicle-mounted TI MMWCAS-RF-EVM (4-chip AWR1243 cascade, 77 GHz, 12 Tx x 16 Rx). Each fault was physically induced via dedicated hardware fixtures (precision yaw-offset jig with digital inclinometer, eccentric-mass DC vibration motor, polycarbonate sheet on PP radome, copper tape on Rx antennas). Severity is gated by measured physical quantities (IMU RMS, inclinometer angle, corner-reflector dB drop, single-tone calibration sweep). Capture protocol: each run is a continuous 5-minute recording at ~10 fps, with the same fault held throughout to provide stable training signal.", "rai:dataAnnotationProtocol": "Per-clip labels (fault_class, severity) are entered once at capture-time by the operator into the RPi sensor-collection script; no manual frame-by-frame annotation. The companion sensors (IMU RMS, board-minus-ambient temperature, GPS speed) provide automatic per-frame regression-quality severity ground truth aligned to each radar frame via timestamp index lookups.", "rai:dataReleaseMaintenancePlan": "Initial release: anonymized dataset hosted on HuggingFace for the WACV double-blind review window. At camera-ready, ownership will be transferred to a stable academic organization account and the dataset will be made public. The full ~324 GB raw .bin captures will be deposited on Zenodo at camera-ready. Future updates (e.g., additional fault types such as thermal stress and RF interference) will be tagged as separate releases.", "rai:personalSensitiveInformation": "The Intel RealSense D435 camera streams (camera2 and camera4 stream IDs) capture environmental scenes and may contain pedestrians or license plates. The released dataset includes only the camera frame paths and timestamps — the camera PNG frames themselves are NOT in this HuggingFace release. The radar data and companion-sensor streams contain no personally-identifiable information.", "rai:dataLimitations": "Rad-R is captured with a single radar device on a single day, with overlapping scenes across faults. Genuine out-of-distribution evaluation along device / day / scene axes is therefore not supported by this release; planned for a future capture campaign. Severity coverage is two levels per fault (mild and severe) rather than the three levels (S1, S2, S3) specified in the taxonomy; additional severity levels may be added in a future release. Two of the six fault types in the taxonomy (thermal stress, RF interference) are not captured in this release.", "rai:dataBiases": "Captures were collected with one operator on one day; no inter-operator variability is sampled in this release. Scene diversity is limited to the test environment used for the May 2026 collection session.", "rai:dataUseCases": "Intended for academic research on (1) hardware fault diagnosis on mmWave automotive radar, (2) raw-ADC representation learning, (3) state-space modeling on complex IQ signals, and (4) sensor-fusion robustness under controlled degradation. Not intended as a substitute for safety-critical certification testing of production radar systems.", "rai:dataSocialImpact": "Improves the reproducibility of radar fault diagnosis research and enables model-based health monitoring of automotive radars, contributing to safer ADAS / autonomous-driving systems. By releasing raw ADC under open license, lowers the barrier for academic groups without specialized data-collection hardware.", "rai:dataSyntheticIndicator": "All captures are real physical recordings from a TI MMWCAS-RF-EVM. No synthetic data, no augmentation applied prior to release.", "rai:dataSources": "All data is original. No third-party datasets are reused, derived from, or aggregated with this release. The radar IQ tensors come exclusively from the TI MMWCAS-RF-EVM cascade radar described above; the synchronized companion-sensor streams come exclusively from the on-rig BNO055 IMU, two DHT22 temperature probes, u-blox NEO-M9N GPS module, and Intel RealSense D435 camera operated together with the radar during the May 2026 collection session.", "rai:dataProvenance": "Provenance chain: (1) raw ADC was captured by mmWave Studio from four DCA1000EVM cards (one per AWR1243 chip) in TI's standard *_data.bin / *_idx.bin format and stored to disk on the capture PC; (2) per-radar-frame wall-clock timestamps were aligned to companion-sensor streams collected on a Raspberry Pi 4 running collect_sensors.py via NTP-synced system clocks; (3) the synced HDF5 files in synced_hdf5/ are the canonical post-sync artifacts produced by build_h5.py; (4) the training_cache.h5 was produced by sampling 200 frames per capture from chunk 0 of the radar binaries, applying TI's standard range FFT (Hann window + DFT along ADC samples) and Doppler FFT (Hann window + DFT along chirp loops + fftshift), incoherently averaging across virtual channels for the RD map, and slicing the Tx=0 portion of the raw IQ for the iq cube. All processing scripts are released alongside the dataset (build_training_cache.py, build_viewer_parquet.py).", "rai:dataPreprocessingProtocol": "Preprocessing for the released training_cache.h5: (a) read raw cascade IQ for the selected frames using the TI MIMO sample-major byte ordering (real/imag interleaved int16 per channel); (b) apply a Hann window along the 256-sample fast-time axis and run an FFT to obtain range-FFT outputs; (c) apply a Hann window along the 64-loop slow-time axis and run an FFT followed by fftshift to obtain the range-Doppler cube; (d) compute |X|^2 averaged across the 192 virtual channels (16 Rx x 12 Tx), convert to dB scale, normalize to [0,1] per frame, and bilinear-resize to 224x224 to produce rd_map; (e) extract the (loops, samples, rx) slab at Tx=0 and store as complex64 to produce iq. No filtering, denoising, or other lossy transformations are applied beyond windowed FFTs and the per-frame RD-map normalization.", "distribution": [ { "@type": "cr:FileObject", "@id": "training-cache", "name": "training_cache.h5", "description": "Pre-processed range-Doppler maps and raw IQ subset, 1800 frames (200 sampled per capture). Recommended entry point for ML training.", "contentUrl": "training_cache.h5", "encodingFormat": "application/x-hdf5", "sha256": "TBD" }, { "@type": "cr:FileSet", "@id": "synced-hdf5-set", "name": "synced_hdf5", "description": "9 sensor-sync HDF5 files, one per capture run. Per-radar-frame timestamps + IMU + DHT22 + GPS streams + camera paths.", "encodingFormat": "application/x-hdf5", "includes": "synced_hdf5/*.h5" } ], "recordSet": [ { "@type": "cr:RecordSet", "@id": "training-cache-records", "name": "training_cache_records", "description": "Each record is one radar frame from one capture, with the RD map, the raw IQ slab (Tx=0 slice), and the fault/severity labels.", "field": [ { "@type": "cr:Field", "@id": "rd_map", "name": "rd_map", "description": "Range-Doppler magnitude map, 224x224, dB scale, normalized per frame.", "dataType": "sc:Float", "source": { "fileObject": { "@id": "training-cache" }, "extract": { "fileProperty": "content" } } }, { "@type": "cr:Field", "@id": "iq", "name": "iq", "description": "Raw complex IQ slab, shape (64 loops, 256 samples, 16 Rx), TDM-MIMO Tx=0 slice.", "dataType": "sc:Float", "source": { "fileObject": { "@id": "training-cache" }, "extract": { "fileProperty": "content" } } }, { "@type": "cr:Field", "@id": "fault_label", "name": "fault_label", "description": "Fault class. 0=healthy, 1=vibration, 2=misalignment, 3=blockage, 4=rx_degradation.", "dataType": "sc:Integer", "source": { "fileObject": { "@id": "training-cache" }, "extract": { "fileProperty": "content" } } }, { "@type": "cr:Field", "@id": "severity_label", "name": "severity_label", "description": "Severity level. 0=healthy baseline, 1=mild, 2=severe.", "dataType": "sc:Integer", "source": { "fileObject": { "@id": "training-cache" }, "extract": { "fileProperty": "content" } } }, { "@type": "cr:Field", "@id": "capture", "name": "capture", "description": "Source capture identifier (one of healthy / yaw0 / yaw2 / vib1 / vib2 / blockage1 / blockage2 / degrade0 / degrade1).", "dataType": "sc:Text", "source": { "fileObject": { "@id": "training-cache" }, "extract": { "fileProperty": "content" } } } ] } ] }