Datasets:
Formats:
json
Size:
1M - 10M
Tags:
driving-simulator
carla
driver-state
human-machine-interaction
in-vehicle-assistant
level-of-autonomy
License:
metadata
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.
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). Code
version used to build and verify this release: d961ce4bd191.
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
labelsconfig isdata/labels.jsonl;data/labels.csvis the identical table in the CSV formbuild_loa_dataset.pytakes (not a config; the build checks the two agree row for row). calibration/is included becauseframes_rawalone cannot regenerateframes_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
- Each row of
labelsdefines a 20 s window[window_start_timestamp, window_end_timestamp]within onesession_id. - Every frame of
frames_preprocessedwhosesession_idmatches and whose wall-clocktimestampfalls 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. - The driver's
user_selected_loabecomes the target — never the system's ownLoA(which is null here anyway, and would be circular in a served session). functionnameandFCDare 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 infcd_config.py.
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 documentedframes_preprocessed: 380,990 rows × 65 columns, as documentedlabels: 1,446 rows × 22 columns, as documentedlabeled: 508,282 rows × 74 columns, as documenteddata/labels.csv(pipeline input) anddata/labels.jsonl(Hub config) hold identical rowsheart_rate_preprocessing.pyrun on the releasedframes_raw+calibration/reproduces the releasedframes_preprocessedon all 380,990 rows and every published columnbuild_loa_dataset.pyrun on that regenerated file + the releasedlabelsreproduces the releasedlabeledon all 508,282 rows and every published columntrain_XLSTM.normalize_rowapplied to every row of the originallabeled_data.jsonland of the releasedlabeled.jsonlgives identical output on all 508,282 rows -- the dropped columns and the bool/int normalisation are invisible to the trainersload_dataset(..., "frames_raw")yields 380,990 rows with the declared types;participantidkeeps its zero paddingload_dataset(..., "frames_preprocessed")yields 380,990 rows with the declared types;participantidkeeps its zero paddingload_dataset(..., "labels")yields 1,446 rows with the declared types;participantidkeeps its zero paddingload_dataset(..., "labeled")yields 508,282 rows with the declared types;participantidkeeps its zero padding