Datasets:
Formats:
json
Size:
1M - 10M
Tags:
driving-simulator
carla
driver-state
human-machine-interaction
in-vehicle-assistant
level-of-autonomy
License:
| 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 | |