---
license: cc-by-4.0
task_categories:
  - image-classification
tags:
  - radar
  - mmwave
  - raw-adc
  - raw-iq
  - complex-signal
  - fault-diagnosis
  - fault-classification
  - automotive
  - sensor-fusion
  - benchmark
  - mimo
  - ti-mmwcas-rf-evm
language:
  - en
size_categories:
  - 1K<n<10K
pretty_name: "Rad-R: Raw-ADC mmWave Automotive Radar Robustness"
configs:
  - config_name: default
    data_files: metadata.parquet
---

# Rad-R: A Real-World Raw-ADC Dataset and Benchmark for mmWave Automotive Radar Robustness

**Anonymous submission to WACV 2027 — 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

| 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 (Croissant 1.0 standard)
radr.py                         # HuggingFace `datasets` loader
```

> **Note on the released IQ.** `training_cache.h5` ships the single-transmitter (Tx=0) IQ slice
> `(1800, 64, 256, 16)` to stay within the HF size quota. The full 192-virtual-channel TDM-MIMO
> cube used for the headline RadrNet results is reconstructed from the raw `*_data.bin` archive with
> the released build script (see below); the full raw archive is deposited on Zenodo at camera-ready.

## How to load (training cache, recommended)

```python
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 (Tx=0 slice)
    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` (and the full 192-channel MIMO cube used for RadrNet).

## 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 (`camera2` and `camera4` stream IDs in the synced HDF5s); PNG paths + per-frame timestamps (the camera PNG frames themselves are not redistributed in this HF release)

## Evaluation tasks

- **Within-clip fault classification** (5-way: healthy + 4 fault classes)
- **Controlled cross-severity generalization** (train one severity, test the other)
- **Severity classification** (3-way: 0=healthy, 1=mild, 2=severe)
- **Chirp-wise anytime** and **few-shot label-budget** protocols
- (Future: cross-day / cross-device / cross-scene OOD splits — deferred to a follow-on capture session)

## Baselines benchmarked (in the paper)

ResNet-18, ViT-Small, ConvNeXt-V2, DeiT-III, Swin-T, PVTv2, EfficientNetV2, and RadrNet (hierarchical Mamba on raw IQ, with capture-invariant and dual-stream variants).

## 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

```bibtex
@inproceedings{radr2027,
  title   = {Rad-R: A Real-World Raw-ADC Dataset and Benchmark for mmWave Automotive Radar Robustness},
  author  = {Anonymous},
  booktitle = {WACV Evaluations \& Datasets Track},
  year    = {2027}
}
```

## Limitations

- **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. Planned for a future release.
- **Raw .bin not redistributed via this HF repo** (300 GB exceeds quota). Available on request; will be deposited on Zenodo at camera-ready.