Datasets:
Formats:
json
Size:
1M - 10M
Tags:
driving-simulator
carla
driver-state
human-machine-interaction
in-vehicle-assistant
level-of-autonomy
License:
File size: 36,235 Bytes
2bac48b 0392fb6 2bac48b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 | ---
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
|