Datasets:
rd_preview dict | frame_idx int32 0 597 | capture stringclasses 9
values | fault_label int32 0 4 | fault_name stringclasses 5
values | severity_label int32 0 2 | severity_name stringclasses 3
values | imu_accel_rms_g float32 0.07 2.42 | imu_gyro_rms_rad_s float32 0.43 41.9 | temp_board_c float32 19 28.6 | gps_speed_mps float32 0.01 10.4 | rd_mean_db float32 0.05 0.28 | rd_std_db float32 0.1 0.18 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
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... | 456 | healthy | 0 | healthy | 0 | s0_healthy | 1.149835 | 7.170743 | 27.799999 | 5.849743 | 0.230031 | 0.139674 |
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... | 420 | healthy | 0 | healthy | 0 | s0_healthy | 0.754953 | 5.364414 | 27.799999 | 5.849743 | 0.178758 | 0.112392 |
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... | 312 | yaw0 | 2 | misalignment | 1 | s1_mild | 1.044433 | 6.566567 | 27.4 | 2.203364 | 0.19855 | 0.120408 |
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... | 18 | yaw2 | 2 | misalignment | 2 | s2_severe | 0.947273 | 4.873715 | 28.299999 | 7.455837 | 0.12647 | 0.160416 |
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... | 288 | vib2 | 1 | vibration | 2 | s2_severe | 1.408166 | 29.926376 | 25.9 | 9.243015 | 0.119311 | 0.156163 |
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... | 183 | blockage1 | 3 | blockage | 1 | s1_mild | 1.266524 | 4.278965 | 25.299999 | 4.192719 | 0.184337 | 0.119962 |
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... | 579 | blockage1 | 3 | blockage | 1 | s1_mild | 0.993584 | 5.519997 | 24.799999 | 0.729482 | 0.058692 | 0.113151 |
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... | 411 | blockage2 | 3 | blockage | 2 | s2_severe | 0.858081 | 5.826213 | 25.200001 | 3.696795 | 0.167799 | 0.142479 |
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... | 522 | healthy | 0 | healthy | 0 | s0_healthy | 1.091947 | 6.161686 | 27.799999 | 7.098813 | 0.214373 | 0.134634 |
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End of preview. Expand in Data Studio
Rad-R: A Real-World Raw-ADC Dataset and Benchmark for mmWave Automotive Radar Robustness
Anonymous submission to NeurIPS 2026 — Evaluations & Datasets Track.
Rad-R is the first publicly released radar dataset combining raw ADC captures from a 4-chip TI MMWCAS-RF-EVM cascade radar (12 Tx × 16 Rx = 192 virtual channels, 77 GHz) with physically induced hardware fault annotations at multiple severity levels, synchronized with companion sensors (BNO055 IMU, DHT22 board/ambient temperature, three GPS streams, Intel RealSense D435 camera with two co-recorded streams).
Captured fault classes (v1.0)
| Class | Severity | Specification | File |
|---|---|---|---|
| Healthy | S0 | Baseline (no induced fault) | healthy.h5 |
| Misalignment | S1 (mild) | 5° yaw via precision jig + inclinometer | yaw0.h5 |
| Misalignment | S2 (severe) | 10° yaw | yaw2.h5 |
| Vibration | S1 (mild) | 20 Hz drive (eccentric DC motor on radar mount) | vib1.h5 |
| Vibration | S2 (severe) | 40 Hz drive | vib2.h5 |
| Blockage | S1 (mild) | 30 % polycarbonate coverage on PP radome | blockage1.h5 |
| Blockage | S2 (severe) | 60 % polycarbonate coverage on PP radome | blockage2.h5 |
| Rx degradation | S1 (mild) | 6/16 Rx antennas copper-taped | degrade0.h5 |
| Rx degradation | S2 (severe) | 10/16 Rx antennas copper-taped | degrade1.h5 |
Severity is gated by measured physical quantities (BNO055 RMS, inclinometer angle, corner-reflector dB drop, single-tone calibration sweep) — not just the procedural setting.
Repository layout
synced_hdf5/ # Sensor-sync metadata, one HDF5 per capture (~25 MB total)
healthy.h5 # Per-radar-frame timestamps + IMU + DHT22 + GPS + camera paths
yaw0.h5 yaw2.h5
vib1.h5 vib2.h5
blockage1.h5 blockage2.h5
degrade0.h5 degrade1.h5
training_cache.h5 # Pre-processed RD maps + raw IQ subset (~2.4 GB)
# 1800 frames (200 sampled per capture)
# rd_map: (1800, 224, 224) f32 dB
# iq: (1800, 64, 256, 16) complex64 (Tx=0 slice)
# fault_label, severity_label, capture, frame_idx
LICENSE # CC BY 4.0
croissant.json # ML metadata (NeurIPS Croissant standard)
radr.py # HuggingFace `datasets` loader
How to load (training cache, recommended)
import h5py
import numpy as np
with h5py.File("training_cache.h5", "r") as f:
rd = np.asarray(f["rd_map"]) # (1800, 224, 224) f32
iq = np.asarray(f["iq"]) # (1800, 64, 256, 16) complex64
fault = np.asarray(f["fault_label"]) # (1800,) int (0=healthy, 1=vib, 2=misalign, 3=block, 4=rxdeg)
sev = np.asarray(f["severity_label"]) # (1800,) int (0=healthy, 1=mild, 2=severe)
capture = np.asarray(f["capture"]).astype(str)
How to load (synced metadata + raw .bin pipeline)
The 9 synced_hdf5/*.h5 files contain per-radar-frame wall-clock timestamps and synchronized companion-sensor streams, but NOT the raw IQ — that lives in TI mmWave Studio *_data.bin capture directories (~324 GB total, available via Zenodo deposit on request).
If you have the raw .bin directories alongside the synced HDF5s, the matlab-to-python pipeline (in the code repo) reads the cascaded ADC, applies range/Doppler FFT, and produces the same RD maps as in training_cache.h5.
Hardware
| Spec | Value |
|---|---|
| Platform | TI MMWCAS-RF-EVM (4× AWR1243) |
| Center frequency | 77 GHz |
| Bandwidth | ~2.5 GHz (256 ADC samples × 79 MHz/μs) |
| Tx × Rx | 12 × 16 = 192 virtual channels |
| Range resolution | 0.06 m |
| Max range | 15.2 m |
| Frame rate | 10 Hz (5 min × 9 captures = 45 min total recording) |
| Companion camera | Intel RealSense D435 (two streams: camera2, camera4) |
| Radome | 1/16″ polypropylene panel (always installed) |
Companion sensors (per-frame aligned)
- Bosch BNO055 IMU — 3-axis accel + gyro + temperature, ~33 Hz
- DHT22 — board + ambient temperature, ~1 Hz
- u-blox NEO-M9N GPS — three decoded streams (legacy, bag-decoded, trips-DB-fused)
- Intel RealSense D435 camera — two co-recorded streams (
camera2andcamera4stream IDs in the synced HDF5s); PNG paths + per-frame timestamps
Evaluation tasks
- In-distribution fault classification (5-way: healthy + 4 fault classes)
- Severity classification (3-way: 0=healthy, 1=mild, 2=severe)
- (Future: cross-day / cross-device / cross-scene OOD splits — deferred to v1.1 capture session)
Baselines benchmarked (in the paper)
ResNet-18, ViT-Small, Raw IQ 1D-CNN, MambaRD, CLIP-B/32 linear probe, OpenCLIP ViT-L/14 linear probe, RadrNet (hierarchical Mamba on raw IQ).
License
CC BY 4.0. You may use, distribute, and adapt this dataset, including for commercial purposes, provided you give attribution to the authors.
Citation
@inproceedings{radr2026,
title = {Rad-R: A Real-World Raw-ADC Dataset and Benchmark for mmWave Automotive Radar Robustness},
author = {Anonymous},
booktitle = {NeurIPS Evaluations \& Datasets Track},
year = {2026}
}
Limitations (v1.0)
- Single capture device, single day, overlapping scenes. Full OOD splits along device / day / scene axes deferred to a follow-on capture campaign.
- Two severity levels per fault (mild / severe). The taxonomy supports up to 4 levels (S0–S3) but only S1–S2 are captured here.
- Thermal stress and RF interference are part of the dataset specification but not yet captured. Scheduled for v1.1.
- Raw .bin not redistributed via this HF repo (300 GB exceeds quota). Available on request; will be deposited on Zenodo at camera-ready.
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