---
license: cc-by-nc-4.0
pretty_name: ProVoice Study 1 - Driver State and Preferred Level of Autonomy
size_categories:
- 100K<n<1M
tags:
- driving-simulator
- carla
- driver-state
- human-machine-interaction
- in-vehicle-assistant
- level-of-autonomy
- ordinal-regression
- personalization
configs:
- config_name: frames_raw
  data_files: data/frames_raw.jsonl
- config_name: frames_preprocessed
  data_files: data/frames_preprocessed.jsonl
- config_name: labels
  data_files: data/labels.jsonl
- config_name: labeled
  data_files: data/labeled.jsonl
dataset_info:
- config_name: frames_raw
  features:
  - name: timestamp
    dtype: string
  - name: session_id
    dtype: string
  - name: participantid
    dtype: string
  - name: traffic_seed
    dtype: int64
  - name: environment
    dtype: string
  - name: secondary_task
    dtype: string
  - name: modeltype
    dtype: string
  - name: state_model
    dtype: string
  - name: w_fcd
    dtype: float64
  - name: face_present
    dtype: bool
  - name: eye_ar
    dtype: float64
  - name: mar
    dtype: float64
  - name: gaze_score
    dtype: float64
  - name: gaze_score_raw
    dtype: float64
  - name: gaze_distracted
    dtype: bool
  - name: blink_rate
    dtype: float64
  - name: blink_rate_raw
    dtype: float64
  - name: yawn_rate
    dtype: float64
  - name: yawn_rate_raw
    dtype: float64
  - name: perclos
    dtype: float64
  - name: perclos_raw
    dtype: float64
  - name: drowsiness_alert
    dtype: bool
  - name: emotion
    dtype: string
  - name: emotion_prob
    dtype: float64
  - name: lab
    list: string
  - name: facebox_misses
    dtype: int64
  - name: facebox_consec_misses
    dtype: int64
  - name: heart_rate
    dtype: float64
  - name: heart_rate_raw
    dtype: float64
  - name: hr_delta
    dtype: float64
  - name: hr_rejected
    dtype: bool
  - name: respiratory_rate
    dtype: float64
  - name: rr_delta
    dtype: float64
  - name: rppg_gaps
    dtype: int64
  - name: rppg_dropped
    dtype: int64
  - name: rppg_suppressed
    dtype: int64
  - name: rppg_harmonic_rejects
    dtype: int64
  - name: speed_kmh
    dtype: float64
  - name: speed_limit_kmh
    dtype: float64
  - name: speed_ratio_max
    dtype: float64
  - name: speed_ratio_limit
    dtype: float64
  - name: throttle
    dtype: float64
  - name: brake
    dtype: float64
  - name: steer
    dtype: float64
  - name: acceleration
    dtype: float64
  - name: gear
    dtype: int64
  - name: reverse
    dtype: bool
  - name: hand_brake
    dtype: bool
  - name: is_junction
    dtype: bool
  - name: is_night
    dtype: bool
  - name: traffic_light_state
    dtype: string
  - name: lead_distance_m
    dtype: float64
  - name: headway_s
    dtype: float64
  - name: precipitation
    dtype: float64
  - name: fog_density
    dtype: float64
  - name: headlight
    dtype: bool
  - name: fog_light
    dtype: bool
  - name: left_indicator
    dtype: bool
  - name: right_indicator
    dtype: bool
  - name: frame_dt_ms
    dtype: float64
  - name: collect_ms
    dtype: float64
  - name: fps_inst
    dtype: float64
  - name: fps_avg
    dtype: float64
- config_name: frames_preprocessed
  features:
  - name: timestamp
    dtype: string
  - name: session_id
    dtype: string
  - name: participantid
    dtype: string
  - name: traffic_seed
    dtype: int64
  - name: environment
    dtype: string
  - name: secondary_task
    dtype: string
  - name: modeltype
    dtype: string
  - name: state_model
    dtype: string
  - name: w_fcd
    dtype: float64
  - name: face_present
    dtype: bool
  - name: eye_ar
    dtype: float64
  - name: mar
    dtype: float64
  - name: gaze_score
    dtype: float64
  - name: gaze_score_raw
    dtype: float64
  - name: gaze_distracted
    dtype: bool
  - name: blink_rate
    dtype: float64
  - name: blink_rate_raw
    dtype: float64
  - name: yawn_rate
    dtype: float64
  - name: yawn_rate_raw
    dtype: float64
  - name: perclos
    dtype: float64
  - name: perclos_raw
    dtype: float64
  - name: drowsiness_alert
    dtype: bool
  - name: emotion
    dtype: string
  - name: emotion_prob
    dtype: float64
  - name: lab
    list: string
  - name: facebox_misses
    dtype: int64
  - name: facebox_consec_misses
    dtype: int64
  - name: heart_rate
    dtype: float64
  - name: heart_rate_raw
    dtype: float64
  - name: hr_delta
    dtype: float64
  - name: hr_rejected
    dtype: bool
  - name: respiratory_rate
    dtype: float64
  - name: rr_delta
    dtype: float64
  - name: rppg_gaps
    dtype: int64
  - name: rppg_dropped
    dtype: int64
  - name: rppg_suppressed
    dtype: int64
  - name: rppg_harmonic_rejects
    dtype: int64
  - name: hr_repaired
    dtype: bool
  - name: hr_repair_method
    dtype: string
  - name: speed_kmh
    dtype: float64
  - name: speed_limit_kmh
    dtype: float64
  - name: speed_ratio_max
    dtype: float64
  - name: speed_ratio_limit
    dtype: float64
  - name: throttle
    dtype: float64
  - name: brake
    dtype: float64
  - name: steer
    dtype: float64
  - name: acceleration
    dtype: float64
  - name: gear
    dtype: int64
  - name: reverse
    dtype: bool
  - name: hand_brake
    dtype: bool
  - name: is_junction
    dtype: bool
  - name: is_night
    dtype: bool
  - name: traffic_light_state
    dtype: string
  - name: lead_distance_m
    dtype: float64
  - name: headway_s
    dtype: float64
  - name: precipitation
    dtype: float64
  - name: fog_density
    dtype: float64
  - name: headlight
    dtype: bool
  - name: fog_light
    dtype: bool
  - name: left_indicator
    dtype: bool
  - name: right_indicator
    dtype: bool
  - name: frame_dt_ms
    dtype: float64
  - name: collect_ms
    dtype: float64
  - name: fps_inst
    dtype: float64
  - name: fps_avg
    dtype: float64
- config_name: labels
  features:
  - name: session_id
    dtype: string
  - name: participantid
    dtype: string
  - name: window_idx
    dtype: int64
  - name: prompt_in_window
    dtype: int64
  - name: window_start_ms
    dtype: int64
  - name: window_end_ms
    dtype: int64
  - name: window_start_timestamp
    dtype: string
  - name: window_end_timestamp
    dtype: string
  - name: selection_timestamp
    dtype: string
  - name: selection_frame
    dtype: int64
  - name: selection_sim_time
    dtype: float64
  - name: selection_speed_kmh
    dtype: float64
  - name: functionname
    dtype: string
  - name: user_selected_loa
    dtype: string
  - name: ambient_gain
    dtype: float64
  - name: ambient_seed
    dtype: int64
  - name: ambient_source
    dtype: string
  - name: environment
    dtype: string
  - name: secondary_task
    dtype: string
  - name: modeltype
    dtype: string
  - name: state_model
    dtype: string
  - name: w_fcd
    dtype: float64
- config_name: labeled
  features:
  - name: timestamp
    dtype: string
  - name: session_id
    dtype: string
  - name: participantid
    dtype: string
  - name: traffic_seed
    dtype: int64
  - name: environment
    dtype: string
  - name: secondary_task
    dtype: string
  - name: modeltype
    dtype: string
  - name: state_model
    dtype: string
  - name: w_fcd
    dtype: float64
  - name: face_present
    dtype: bool
  - name: eye_ar
    dtype: float64
  - name: mar
    dtype: float64
  - name: gaze_score
    dtype: float64
  - name: gaze_score_raw
    dtype: float64
  - name: gaze_distracted
    dtype: bool
  - name: blink_rate
    dtype: float64
  - name: blink_rate_raw
    dtype: float64
  - name: yawn_rate
    dtype: float64
  - name: yawn_rate_raw
    dtype: float64
  - name: perclos
    dtype: float64
  - name: perclos_raw
    dtype: float64
  - name: drowsiness_alert
    dtype: bool
  - name: emotion
    dtype: string
  - name: emotion_prob
    dtype: float64
  - name: lab
    list: string
  - name: facebox_misses
    dtype: int64
  - name: facebox_consec_misses
    dtype: int64
  - name: heart_rate
    dtype: float64
  - name: heart_rate_raw
    dtype: float64
  - name: hr_delta
    dtype: float64
  - name: hr_rejected
    dtype: bool
  - name: respiratory_rate
    dtype: float64
  - name: rr_delta
    dtype: float64
  - name: rppg_gaps
    dtype: int64
  - name: rppg_dropped
    dtype: int64
  - name: rppg_suppressed
    dtype: int64
  - name: rppg_harmonic_rejects
    dtype: int64
  - name: hr_repaired
    dtype: bool
  - name: hr_repair_method
    dtype: string
  - name: speed_kmh
    dtype: float64
  - name: speed_limit_kmh
    dtype: float64
  - name: speed_ratio_max
    dtype: float64
  - name: speed_ratio_limit
    dtype: float64
  - name: throttle
    dtype: float64
  - name: brake
    dtype: float64
  - name: steer
    dtype: float64
  - name: acceleration
    dtype: float64
  - name: gear
    dtype: int64
  - name: reverse
    dtype: bool
  - name: hand_brake
    dtype: bool
  - name: is_junction
    dtype: bool
  - name: is_night
    dtype: bool
  - name: traffic_light_state
    dtype: string
  - name: lead_distance_m
    dtype: float64
  - name: headway_s
    dtype: float64
  - name: precipitation
    dtype: float64
  - name: fog_density
    dtype: float64
  - name: headlight
    dtype: bool
  - name: fog_light
    dtype: bool
  - name: left_indicator
    dtype: bool
  - name: right_indicator
    dtype: bool
  - name: frame_dt_ms
    dtype: float64
  - name: collect_ms
    dtype: float64
  - name: fps_inst
    dtype: float64
  - name: fps_avg
    dtype: float64
  - name: functionname
    dtype: string
  - name: FCD
    struct:
    - name: Safety Risk
      dtype: int64
    - name: Increased Safety
      dtype: int64
    - name: Relevance
      dtype: int64
    - name: Magicality
      dtype: int64
    - name: Privacy
      dtype: int64
    - name: Trust
      dtype: int64
    - name: Time Consumption
      dtype: int64
    - name: Repetitiveness
      dtype: int64
    - name: Situational Context
      dtype: int64
    - name: Social Risk
      dtype: int64
    - name: Urgency
      dtype: int64
    - name: Complexity
      dtype: int64
  - name: segment_id
    dtype: string
  - name: user_loa
    dtype: string
  - name: Level_1
    dtype: int64
  - name: Level_2
    dtype: int64
  - name: Level_3
    dtype: int64
  - name: Level_4
    dtype: int64
  - name: Level_5
    dtype: int64
---

# ProVoice study 1 — driver state, vehicle context and preferred Level of Autonomy

Driving-simulator data from the population data collection of the ProVoice /
ProActivity project (CARLA 0.10): **12 drivers × 2 sessions**, ~20 Hz
multimodal driver-state and vehicle frames, and **1,446 driver-assigned
Level-of-Autonomy (LoA) labels** stating how autonomously an in-vehicle
assistant should act on a given task. Drivers were prompted every 20 s about
two randomly drawn in-vehicle tasks and marked, for each, the LoA(s) they
would accept (0 = do nothing … 4 = act autonomously).

| Config | What it is | Use it for |
| --- | --- | --- |
| `labels` | one row per prompt: the driver's answer plus the window it refers to | the ground truth; join key for everything else |
| `frames_preprocessed` | every logged frame with the heart-rate channel repaired offline | driver-state modelling (this is what the released models were trained on) |
| `frames_raw` | the same frames exactly as logged live | provenance; comparing the live vs. offline HR filter |
| `labeled` | `frames_preprocessed` joined to `labels`, **frames duplicated once per label** | one-file training input; read §C before counting rows |

`data/labels.csv` is the same table as the `labels` config in the CSV form
`scripts/build_loa_dataset.py` reads (the Hub loads one file format per repo).
`calibration/` (not a config) holds each driver's 60 s calibration baseline and
per-tick calibration log — inputs of the heart-rate repair, shipped so that
`frames_preprocessed` can be regenerated from `frames_raw`.

```python
from datasets import load_dataset
labels = load_dataset("danipulidoe/proactivity_preference_dataset", "labels", split="train")
frames = load_dataset("danipulidoe/proactivity_preference_dataset", "frames_preprocessed", split="train")
```

## Reproducing the pipeline

Files are in the exact formats the code reads (JSONL frames, CSV labels), so
the project's documented commands run on them unchanged (code: [https://github.com/DaniPulidoE/ProActivity_Personalization](https://github.com/DaniPulidoE/ProActivity_Personalization)). Code
version used to build and verify this release: `d961ce4bd191`.

```bash
hf download danipulidoe/proactivity_preference_dataset --repo-type dataset --local-dir provoice_study1

# 1. frames_raw + calibration  ->  frames_preprocessed   (heart-rate repair)
python data_preprocessing/heart_rate_preprocessing.py \
    --in-data  provoice_study1/data/frames_raw.jsonl \
    --calib-dir provoice_study1/calibration \
    --out-data provoice_study1/data/frames_preprocessed.jsonl --no-write-calibration

# 2. frames_preprocessed + labels  ->  labeled            (window alignment)
python scripts/build_loa_dataset.py \
    --raw    provoice_study1/data/frames_preprocessed.jsonl \
    --labels provoice_study1/data/labels.csv \
    --out-jsonl provoice_study1/data/labeled.jsonl --out-fcd fcd_out.csv

# 3. labeled  ->  population model
python -m ProVoice.models.train_XLSTM --in provoice_study1/data/labeled.jsonl \
    --out trained_models/state_xlstm.pt --loss corn
```

Steps 1 and 2 were re-run on the released files at build time and reproduce the released `frames_preprocessed` and `labeled` **row for row** (see "Verification" at the end). The columns listed under "excluded" in the schema
below are absent from the released files; none of them is read by any step.

| File | SHA-256 |
| --- | --- |
| `calibration/calibration_001.json` | `33ca2fa2ea4965b10933327ddb8760cd646f45697df403f21a61637947c8067c` |
| `calibration/calibration_002.json` | `b5f694ea0d55a862d8c8d34d332276c8ef0408308ce975fbd4b1c79fe59ff3b2` |
| `calibration/calibration_003.json` | `4b4d0b9e9be04d68d6e3d81ebe513ac572eccc89ac750199b31a35a21fc2831a` |
| `calibration/calibration_004.json` | `acca11fc08a959e3dad41f051f13d6964b37bd991d9c66ce3f25300a02537fba` |
| `calibration/calibration_005.json` | `7d63dc5f40a04520f7078dc690b11f122792ec3bb7ed8ad7664dbb6c1e6483e5` |
| `calibration/calibration_006.json` | `2c7b2eb86bdc45a44b9ade7c0e791f0eaa49b40b02e20b03e8e0b7649a55e67c` |
| `calibration/calibration_007.json` | `cfd1f551b75a3c4550bcfccc502752ab682a38db91d2f18f70b0a7cc2ed737b5` |
| `calibration/calibration_008.json` | `0d362cf1dd78289eb97c10bcb2bf9cef139e7c8f6d5ce81418291e26df082a7f` |
| `calibration/calibration_009.json` | `535e2a3ab671a8a319bee4bc1140f2b4a30bbe6fc9e7cf7ecc05bbeaf65cbe2e` |
| `calibration/calibration_010.json` | `11421b92d2672a7afbe3606b9a26cdfe2df91a3890b9942800b1c921f3bf1d20` |
| `calibration/calibration_011.json` | `70315e1020313369550f5a56d75b76b75f4de19ef7315362a7e6165c30479a94` |
| `calibration/calibration_012.json` | `c55e17da9b46e28be3d840d9a81ee2316b1e54d94b83a26d38880396050efc6e` |
| `calibration/calibration_logs/log_calibration_001.csv` | `bb12243a53865ead7334755fbaab6e4024df5e776b4e8953746678e1e654ce4c` |
| `calibration/calibration_logs/log_calibration_002.csv` | `090a5efdda145e1f9b6b68b9b71aee65f93ecd65e6c4ff40418086e33d521981` |
| `calibration/calibration_logs/log_calibration_003.csv` | `29ce82637604bd43e538a7cc616bc9fd0706c07d7ef507f528d8b2075344538d` |
| `calibration/calibration_logs/log_calibration_004.csv` | `a2ff46535d906e060272f411c216634b306b1cdff51cd750f8c16599d4f36ca0` |
| `calibration/calibration_logs/log_calibration_005.csv` | `e1937182fd6f8aa3db5aaf84975ab5414c53f02a2bdd7acf18fc34de2f4b34ce` |
| `calibration/calibration_logs/log_calibration_006.csv` | `b953adde5b2ce1b250a808c8eb439bce38c78b06fa07c5a3e6a0bfa8bb95ee66` |
| `calibration/calibration_logs/log_calibration_007.csv` | `88a3468ab3c896b02f13efaef79a3f40e959f649bb798493c4ac41a58692f80b` |
| `calibration/calibration_logs/log_calibration_008.csv` | `3fa3da9842a22719c3e073f8afad990290a244eba297c0e8c701c2a07aec95ec` |
| `calibration/calibration_logs/log_calibration_009.csv` | `b3a00a1bcc6a7608e97c381cbc7c8d96048ac9fe6f328824e81158877a0f4f9b` |
| `calibration/calibration_logs/log_calibration_010.csv` | `d54e979d322ac227b9338d19fecec9718e0b5a406961e28a066387eea109b1ed` |
| `calibration/calibration_logs/log_calibration_011.csv` | `b138fc965b2dbccd5cc44dd3c0004d861551491758c07582a784128e6b2cbba9` |
| `calibration/calibration_logs/log_calibration_012.csv` | `83f2b615a8a63f110794db332b5ff546b068deae0e024362906fbe234ce6bda0` |
| `data/frames_preprocessed.jsonl` | `0b8976a4630bc798cab3140240996080beecf7c6e84d1a5cfe82a884ca38af68` |
| `data/frames_raw.jsonl` | `ec5f0aced5ef84671ea3cbc004286cf334cae8c8b4868b93f88445a9931ee65c` |
| `data/labeled.jsonl` | `b2db598a951483af630af11ed0eb7bce2610527ca05ae8d2b1307c07d0675629` |
| `data/labels.csv` | `e69cfcbee93e9f99a2f333bb991c9dadc6e93df50b677b95c1c8398f227e35e5` |
| `data/labels.jsonl` | `8db7499f684e72b80e8c1089d449bc704c241f4a3b75437939df6ac9868ae7c8` |

---

Column inventory of the population data collection (12 drivers × 2 sessions,
2026-08), computed over every row of `data/study1_data/` on 2026-09-13.
Types are the **declared types** — written into the dataset card as
`dataset_info.features` so `load_dataset` casts instead of inferring. Where the
source file is mixed the JSON types are given in parentheses; the release
normalises them (int `0` → `false`, int → float) because Arrow inference fails
on these files (`hr_repair_method` null→string) or is order-dependent
(`is_night`/`is_junction` bool/int). The trainers are indifferent to the
normalisation — see "Verification" below.

Files in the release:

| HF config | Source file | Published as | Rows | Cols | Unit of one row |
| --- | --- | --- | --- | --- | --- |
| `frames_raw` | `raw_data.jsonl` | `data/frames_raw.jsonl` | 380,990 | 63 | one DataCollector tick (~20 Hz), HR as filtered live |
| `frames_preprocessed` | `preprocessed_data.jsonl` | `data/frames_preprocessed.jsonl` | 380,990 | 65 | same frames, HR rebuilt offline by `heart_rate_preprocessing.py` (+2 provenance cols) |
| `labels` | `user_loa_labels.csv` | `data/labels.jsonl` **and** `data/labels.csv` | 1,446 | 22 | one driver prompt (two per 20 s window) |
| `labeled` | `labeled_data.jsonl` | `data/labeled.jsonl` | 508,282 | 74 | one (frame, label) pair — the training file; **frames are duplicated once per label**, see §C |
| — | `calibration_data_study/` | `calibration/calibration_<pid>.json`, `calibration/calibration_logs/log_calibration_<pid>.csv` | 12 + 12 files | — | per-driver 60 s calibration baseline and per-tick log — inputs of the HR repair |

**The release is built for full reproducibility**, which fixes the formats:
files are published in exactly the shapes the pipeline reads (JSONL frames,
CSV labels), so `heart_rate_preprocessing.py`, `build_loa_dataset.py` and every
trainer run on them unchanged. Two consequences:

- The Hub applies a single file format to all configs of one repo, so the
  `labels` config is `data/labels.jsonl`; `data/labels.csv` is the identical
  table in the CSV form `build_loa_dataset.py` takes (not a config; the build
  checks the two agree row for row).
- `calibration/` is included because `frames_raw` alone cannot regenerate
  `frames_preprocessed`: the HR repair needs each driver's calibration baseline
  and per-tick calibration log.

`scripts/upload_study1_hf.py` stages the release, refuses to upload if the
row/column counts differ from this table, and with `--verify-chain` re-runs
the pipeline on the staged inputs — the results are recorded in the card:

**Verification (2026-09-13, code `14a3be2`).** `heart_rate_preprocessing.py`
on the released `frames_raw` + `calibration/` reproduces the released
`frames_preprocessed` on all 380,990 rows and every published column;
`build_loa_dataset.py` on that output + `labels.csv` reproduces the released
`labeled` on all 508,282 rows; `train_XLSTM.normalize_row` applied to the
original `labeled_data.jsonl` and to the released `labeled.jsonl` is identical
on every row, so a model trained on the release is the model trained on the
original. Each config also loads through `datasets` with the declared types
and `participantid` keeps its zero padding.

The two frame files are byte-identical in every column outside the rPPG block
(0 differing cells over 380,990 rows); only `heart_rate`, `hr_delta` and the
`hr_repair_*` columns differ.

---

## A. Frame files — `frames_raw` (63 cols) / `frames_preprocessed` (65 cols)

Columns marked **P** exist only in `frames_preprocessed`.

### A.1 Identity / session context

| Column | Type | Null | Notes |
| --- | --- | --- | --- |
| `timestamp` | string | 0 % | Wall-clock `HH:MM:SS.mmm`, local time, **no date**. Join to `labels` via `session_id` + `window_start_timestamp`/`window_end_timestamp`. |
| `session_id` | string | 0 % | UUID; 24 distinct |
| `participantid` | string | 0 % | `001`–`012` |
| `traffic_seed` | int | 0 % | CARLA traffic-scenario seed (`TRAFFIC_SEED_PLAN` in `start_experiment.py`) |
| `environment` | string | 0 % | constant `city` |
| `secondary_task` | string | 0 % | constant `none` |
| `modeltype` | string | 0 % | constant `combined` (run config) |
| `state_model` | string | 0 % | constant `xlstm` (run config) |
| `w_fcd` | float | 0 % | constant `0.7` (run config) |

### A.2 Face / driver state (MediaPipe FaceLandmarker, EmotiEffLib, YOLO26)

| Column | Type | Null | Notes |
| --- | --- | --- | --- |
| `face_present` | bool | 0 % | landmarker found a face |
| `eye_ar` | float | 0 % | eye aspect ratio (model feature `ear`) |
| `mar` | float | 0 % | mouth aspect ratio |
| `gaze_score` | float | 0 % | z-scored against the 180 s calibration baseline |
| `gaze_score_raw` | float | 0 % | uncalibrated gaze score |
| `gaze_distracted` | bool | 0 % | `gaze_score` above calibrated threshold (mean + 2.5·std) |
| `blink_rate` | float | 0 % | normalized (Poisson) against calibration mean |
| `blink_rate_raw` | float | 0 % | blinks · min⁻¹ |
| `yawn_rate` | float | 0 % | normalized |
| `yawn_rate_raw` | float | 0 % | yawns · min⁻¹ |
| `perclos` | float | 0 % | z-scored |
| `perclos_raw` | float | 0 % | fraction of time eyes closed |
| `drowsiness_alert` | bool | 0 % | PERCLOS + MAR rule |
| `emotion` | string | 0.2 % | one of `angry, disgust, fear, happy, sad, surprise, neutral`; `null` = no reading (no face / classifier failure) |
| `emotion_prob` | float | 0.2 % | confidence of `emotion`; `null` iff `emotion` is null |
| `lab` | list\<string\> | 0 % | YOLO26 distraction classes present: subset of `face`, `phone`, `drink`; may be empty |
| `facebox_misses` | int | 0 % | face-box worker detector misses (cumulative) |
| `facebox_consec_misses` | int | 0 % | consecutive misses; box goes stale at 4 s |

### A.3 rPPG heart rate / respiration (MMRPhys, SCAMPS LEF 72×72)

| Column | Type | Null | Notes |
| --- | --- | --- | --- |
| `heart_rate` | float | 1.6 % | bpm. `frames_raw`: live-filtered reading. `frames_preprocessed`: offline-repaired — differs on 80,156 frames (21 %) |
| `heart_rate_raw` | float | 1.6 % | unfiltered estimator output |
| `hr_delta` | float | 1.6 % | `heart_rate` standardized against the per-driver baseline (median / SD of cleaned calibration readings, floor 5 bpm); recomputed in `frames_preprocessed` |
| `hr_rejected` | bool | 1.6 % | live 2f-harmonic filter rejected `heart_rate_raw` |
| `respiratory_rate` | float | 1.6 % | breaths · min⁻¹. **Not a model input** — RGB respiration from the synthetic-trained checkpoint was judged noise |
| `rr_delta` | float | 1.6 % | standardized RR (median / MAD baseline) |
| `rppg_gaps` | int | 0 % | look-away discontinuities > 1 s that spliced the model window |
| `rppg_dropped` | int | 0 % | frames lost to a full rPPG queue |
| `rppg_suppressed` | int | 0 % | readings flagged as gap-contaminated (never discarded in study 1) |
| `rppg_harmonic_rejects` | int | 0 % | probable 2f rejections |
| **P** `hr_repaired` | bool | 0 % | `heart_rate` was rewritten offline |
| **P** `hr_repair_method` | string | 79 % | `folded` (45,023 — 2f harmonic halved), `interpolated` (33,597 — outlier replaced), `carried` (1,536); null when not repaired |

### A.4 Vehicle / world (CARLA 0.10, via the vehicle-state bridge)

| Column | Type | Null | Notes |
| --- | --- | --- | --- |
| `speed_kmh` | float | 0 % | ego speed |
| `speed_limit_kmh` | float (int/float) | 0 % | `0` on the first 20 frames of each session (bridge not yet connected), else `30.0` |
| `speed_ratio_max` | float | 0 % | `speed_kmh / 150` |
| `speed_ratio_limit` | float (int/float) | 0 % | `speed_kmh / speed_limit_kmh`; `-1` when the limit is unknown |
| `throttle` | float | 0 % | [0, 1] |
| `brake` | float (int/float) | 0 % | [0, 1] |
| `steer` | float (int/float) | 0 % | [−1, 1] |
| `acceleration` | float | 0 % | magnitude, m · s⁻² |
| `gear` | int | 0 % | |
| `reverse` | bool | 0 % | |
| `hand_brake` | bool | 0 % | constant `False` |
| `is_junction` | bool (bool/int) | 0 % | ego waypoint is in a junction; int `0` only on the first 20 frames of each session |
| `is_night` | bool (bool/int) | 0 % | constant `False` (sun above horizon in every session) |
| `traffic_light_state` | string | 0.1 % | `Red` / `Yellow` / `Green` |
| `lead_distance_m` | float (int/float) | 0 % | distance to lead vehicle in ego lane, **scaled `/100`** (so ≈ [0, 1]); `-1` = no lead vehicle within 100 m (73 % of frames). Sentinel, not zero — 0 would mean contact |
| `headway_s` | float | 79 % | time headway, s; `null` = no lead vehicle, or ego below walking pace |
| `precipitation` | float (int/float) | 0 % | constant `0` (no weather in CARLA 0.10) |
| `fog_density` | float | 0 % | constant `0.0` |
| `headlight` | bool | 0 % | constant `False` |
| `fog_light` | bool | 0 % | constant `False` |
| `left_indicator` | bool | 0 % | constant `False` |
| `right_indicator` | bool | 0 % | constant `False` |

### A.5 Pipeline timing

Present on most frames, absent on a few early ticks per session (hence two key
sets in the JSONL).

| Column | Type | Notes |
| --- | --- | --- |
| `frame_dt_ms` | float | inter-frame gap |
| `collect_ms` | float | perception loop time for this tick |
| `fps_inst` | float | instantaneous achieved rate |
| `fps_avg` | float | running mean achieved rate — use to identify participant 001's ~4 Hz warm-up (session `77b516f6`, windows 1–12) |

---

## B. `labels` — `user_loa_labels.csv` (1,446 rows, 22 cols)

One row per prompt. Each 20 s window carries two prompts (`--random-function`),
so 723 windows → 1,446 rows.

| Column | Type | Notes |
| --- | --- | --- |
| `session_id` | string | join key to frames |
| `participantid` | string | `001`–`012` |
| `window_idx` | int | 1-based window index within the session |
| `prompt_in_window` | int | `1` or `2` |
| `window_start_ms` | int | window start, CARLA sim time |
| `window_end_ms` | int | window end, CARLA sim time (= start + 20,000) |
| `window_start_timestamp` | string (ISO 8601) | wall-clock window start — join to frame `timestamp` |
| `window_end_timestamp` | string (ISO 8601) | wall-clock window end |
| `selection_timestamp` | string (ISO 8601) | when the driver submitted the answer |
| `selection_frame` | int | CARLA frame at submission |
| `selection_sim_time` | float | CARLA sim time at submission, s |
| `selection_speed_kmh` | float | ego speed at submission |
| `functionname` | string | **the prompted task**, one of five: `Provide traffic news` (307), `Respond to a text message` (296), `Respond to a phone call` (292), `Change song` (276), `Provide weather update` (275) |
| `user_selected_loa` | string | **ground truth.** Single LoA `0`–`4` (1,348 rows), or a `;`-joined set of acceptable LoAs (98 rows, 6.8 %): `1;2` ×40, `0;1` ×30, `2;3` ×17, `3;4` ×5, `0;1;2;3` ×4, `1;2;3` ×1, and one non-contiguous `0;3` |
| `ambient_gain` | float | ambient-audio config (constant `0.35`) |
| `ambient_seed` | int | ambient-audio config (constant `0`) |
| `ambient_source` | string | ambient-audio config (constant) |
| `environment` | string | constant `city` |
| `secondary_task` | string | constant `none` |
| `modeltype` | string | constant `combined` |
| `state_model` | string | constant `xlstm` |
| `w_fcd` | float | constant `0.7` |

---

## C. `labeled` — `labeled_data.jsonl` (508,282 rows, 74 cols)

This is the file the xLSTM population model and every per-driver adaptation
were trained on. It is **fully derived** from `frames_preprocessed` and
`labels` by `scripts/build_loa_dataset.py`; it is included so the training
input is available as-is, without requiring the join to be reproduced.

### C.1 How it was generated

1. Each row of `labels` defines a 20 s window
   `[window_start_timestamp, window_end_timestamp]` within one `session_id`.
2. Every frame of `frames_preprocessed` whose `session_id` matches and whose
   wall-clock `timestamp` falls inside that window is attached to the label.
   Frames outside every window (calibration, the gaps between windows) are
   dropped: 254,141 of 380,990 frames are labelled.
3. The driver's `user_selected_loa` becomes the target — never the system's
   own `LoA` (which is null here anyway, and would be circular in a served
   session).
4. `functionname` and `FCD` are taken from the **label row**, not the frame.
   The collector stamps the CLI default (`Adjust seat positioning`) on every
   frame; the label records the task the driver was actually asked about, and
   its FCD vector is looked up in `fcd_config.py`.

```bash
python scripts/build_loa_dataset.py \
    --raw data/preprocessed_data.jsonl \
    --labels data/user_loa_labels.csv \
    --out-jsonl data/labeled_data.jsonl \
    --out-fcd data/processed_data/fcd_out.csv
```

### C.2 Why rows are duplicated

Under `--random-function` the drive UI asks **two prompts per 20 s window**,
about two different tasks, and the driver answers each separately. Both label
rows share the same window bounds, so they select the same frames. Each
frame is therefore emitted **once per label**: identical driver-state and
vehicle features, different `functionname`, `FCD`, `user_loa` and
`segment_id`. Taking only the first match would discard half the labels.

Consequences for anyone using the file:

- **The unit of analysis is `segment_id`, not the row.** 1,446 segments =
  1,446 labels; 508,282 rows = 254,141 distinct frames × 2. Row counts
  double-count frames; `(session_id, timestamp)` identifies a physical frame.
- Frames per segment: min 80, median 381, max 396 (the 80-frame segments are
  participant 001's ~4 Hz warm-up windows, see §A.5).
- A segment carries one label; the frames within it are one time series.
  Train/validation/test splits must be made at the **segment** (or window)
  level — splitting by row leaks a window's frames across splits.
- The two segments of one window are not independent samples of driver state:
  they differ only in the task asked about. Treat them as such in any
  analysis of the state features.

### C.3 Columns

All 65 `frames_preprocessed` columns (§A) are passed through verbatim, plus
two columns taken from the **label row** (the frame files do not carry them —
see §D for why the collector's own `functionname`/`FCD` were dropped):

| Column | Type | Notes |
| --- | --- | --- |
| `functionname` | string | the prompted task (five values, as in `labels.functionname`) |
| `FCD` | struct\<12 × int\> | the task's FCD vector, keys `Safety Risk, Increased Safety, Relevance, Magicality, Privacy, Trust, Time Consumption, Repetitiveness, Situational Context, Social Risk, Urgency, Complexity`, values 1–5; identical for all rows of a segment |

plus seven columns added by the builder:

| Column | Type | Notes |
| --- | --- | --- |
| `segment_id` | string | `<session_id>\|winNNNpM` (`NNN` = window index, `M` = prompt 1/2) — the unit of one label. **Count these, not rows.** |
| `user_loa` | string | copy of `labels.user_selected_loa` for this segment (`;`-joined sets preserved) |
| `Level_1` … `Level_5` | int | multi-hot of `user_loa`; `Level_k` = 1 iff LoA `k−1` is in the set |


---

## D. Columns excluded from the release

Present in the on-disk files, deliberately not uploaded.

| File | Column(s) | Why |
| --- | --- | --- |
| frames (both) | `functionname` | Constant `Adjust seat positioning` — the CLI default ProVoice was started with, **not** the task the driver was prompted about. Non-null and plausible-looking, so more misleading than an empty column. The prompted task is `labels.functionname` (and `labeled.functionname`, which is taken from the label row). |
| frames (both) | `LoA`, `FCD` | 100 % null — no model was served in study 1. The collector emits them because in a served session they hold the decision in force at that frame. |
| frames_preprocessed, labeled | `hr_repair_reason` | Free-text audit string for each HR repair (the rule that fired, e.g. `2f harmonic of session 52`). Redundant with `hr_repaired` + `hr_repair_method` for any downstream use; kept only in the local files. |
| labeled | `LoA` | 100 % null, passed through from the frames (see above). `FCD` is **kept** in `labeled` — the builder overwrites it with the prompted task's vector (§C.3). |
| labels | `emotion`, `system_action`, `system_level`, `system_loa`, `system_message`, `system_probs`, `system_profile`, `system_fallback`, `system_fallback_reason`, `system_fcd` | 100 % empty — no system prediction was shown to the driver in study 1. |

Under `calibration/`, only the 12 study participants' `calibration_<pid>.json`
and `calibration_logs/log_calibration_<pid>.csv` are released: the
`calibration_<pid>_preprocessed.json` files are *outputs* of
`heart_rate_preprocessing.py` (the reproduction chain regenerates them), and
participant `998` is a rig test, not part of the study.
---

## Verification (performed at build time by `scripts/upload_study1_hf.py`)

- `frames_raw`: 380,990 rows × 63 columns, as documented
- `frames_preprocessed`: 380,990 rows × 65 columns, as documented
- `labels`: 1,446 rows × 22 columns, as documented
- `labeled`: 508,282 rows × 74 columns, as documented
- `data/labels.csv` (pipeline input) and `data/labels.jsonl` (Hub config) hold identical rows
- `heart_rate_preprocessing.py` run on the released `frames_raw` + `calibration/` reproduces the released `frames_preprocessed` on all 380,990 rows and every published column
- `build_loa_dataset.py` run on that regenerated file + the released `labels` reproduces the released `labeled` on all 508,282 rows and every published column
- `train_XLSTM.normalize_row` applied to every row of the original `labeled_data.jsonl` and of the released `labeled.jsonl` gives identical output on all 508,282 rows -- the dropped columns and the bool/int normalisation are invisible to the trainers
- `load_dataset(..., "frames_raw")` yields 380,990 rows with the declared types; `participantid` keeps its zero padding
- `load_dataset(..., "frames_preprocessed")` yields 380,990 rows with the declared types; `participantid` keeps its zero padding
- `load_dataset(..., "labels")` yields 1,446 rows with the declared types; `participantid` keeps its zero padding
- `load_dataset(..., "labeled")` yields 508,282 rows with the declared types; `participantid` keeps its zero padding