pretty_name: DriveMotion
license: cc-by-4.0
task_categories:
- time-series-forecasting
- keypoint-detection
language:
- en
tags:
- motion-forecasting
- human-pose
- driver-monitoring
- in-cabin
- autonomous-driving
- skeleton
- multimodal
- benchmark
size_categories:
- 100K<n<1M
🚗 DriveMotion
A Large-Scale Multi-Source Benchmark for Driver Motion Sequence Modeling & Forecasting
400 hours of in-cabin driver motion · 360 drivers · 9,010 sequences · 680,082 forecasting windows 133 whole-body keypoints @ 10 Hz · synchronized CAN & exterior context · 5 camera-view types
🎬 What does it look like?
Every sequence in DriveMotion is released as a privacy-reduced skeleton motion video plus a standardized keypoint tensor — the driver's behavior is preserved, appearance identity is not.
| Fleet (BATON) | In-the-wild web | AIDE (semantic labels) |
|---|---|---|
 |
 |
 |
| continuous routes, CAN, head pose | varied viewpoints & visibility | behavior / emotion annotations |
🧭 Why DriveMotion?
Driver-monitoring datasets are built for recognizing actions from short clips. Human-motion forecasting benchmarks live in labs or on sidewalks. Neither covers the question an in-cabin system actually faces: what will the driver's body do in the next few seconds?
DriveMotion standardizes three heterogeneous sources into one motion representation and one forecasting protocol:
| Source | Role | Sequences | Hours | What it brings |
|---|---|---|---|---|
| BATON fleet | temporal backbone | 1,347 routes | 320 h | continuous minutes-to-hours routes, time-aligned CAN, device-grounded head pose, road-camera context |
| Web corpus | observation diversity | 4,765 spans | 78 h | front / side / back viewpoints, five visibility levels, creator-diverse cabins |
| AIDE (re-extracted) | semantics | 2,898 clips | 2.4 h | behavior & emotion labels in the same sequence format |
All sources pass through one extraction trunk (RTMW whole-body pose, resampling to 10 Hz, normalization, validity estimation, quality scoring) and differ only in how the driver is found:
📦 Unified representation
One sequence = one .npz + one .meta.json, on a fixed 10 Hz grid:
| Field | Shape | Meaning |
|---|---|---|
t / t_actual |
[N] |
grid time / actual source-video time (s) |
kpts |
[N, 133, 3] |
COCO-WholeBody layout (x, y, score), normalized image coords; 127 slots active in-cabin |
mask |
[N, 133] |
per-joint validity — occlusion is explicit, never imputed |
head |
[N, 3] |
yaw / pitch / roll (deg); device-derived on BATON, vision-based elsewhere |
can |
[N, 4] |
speed, steering, throttle, brake (BATON) |
part_valid |
[N, 6] |
per-part coverage flags |
quality |
[N] |
per-frame extraction quality score |
meta.json records source, view type, visibility level, driver id, native fps/resolution, and
absolute source timestamps — the full provenance of every frame.
import numpy as np, json
d = np.load("motion/web/<seq>.npz")
meta = json.load(open("motion/web/<seq>.meta.json"))
kpts, mask = d["kpts"], d["mask"] # [N,133,3], [N,133]
print(meta["view"], meta["driver_visibility"], kpts.shape)
🎯 The forecasting benchmark
Task: observe 8 s of driver motion → predict the next 4 s of keypoint trajectories and head pose (10 Hz), in a canonical torso frame. Exterior scene features (2 Hz frozen ResNet-50 embeddings of the road view) are an optional input. CAN is never an input — it is used only offline to build evaluation windows.
Naturalistic driving is dominated by stillness, so uniform evaluation mostly scores "nothing happens". DriveMotion therefore anchors its primary protocol on vehicle-dynamics transitions (76,026 maneuver initiations mined from CAN):
- Protocol A — dynamics-anchored forecasting: pre-maneuver / post-maneuver / stable-control strata (6,138 / 6,238 / 4,262 test windows). Arm motion in pre-maneuver windows is 3.4× that of matched stable driving.
- Protocol B — robustness under observation shift: train on the fleet, evaluate on held-out web creators across viewpoints and visibility levels.
- Metrics: MPJPE@4s on 23 cross-view-stable points, plus Part-State F1@2s — does the head / torso / each arm stay still, move a little, or move a lot?
- Identity-disjoint splits, fixed hashed evaluation subsets, frozen state thresholds — all released with the toolkit.
Reference results (dynamics-anchored test set, 16,638 windows)
| Model | MPJPE@4s ↓ | Part-State F1@2s ↑ |
|---|---|---|
| Zero-motion (persistence) | 7.75 | 0.215 |
| GRU seq2seq | 6.83 | 0.275 |
| siMLPe | 6.85 | 0.227 |
| Transformer ED | 6.75 | 0.282 |
| Transformer ED (+ctx, enriched) | 6.95 | 0.309 |
| Transformer-L | 6.63 | 0.287 |
| Transformer-XL | 6.62 | 0.284 |
| CVAE | 6.84 | 0.257 |
| DDPM (+ctx, enriched) | 8.86 | 0.299 |
| AR motion-token LM (enriched) | 8.16 | 0.458 |
| LLM backbone (Llama-3B) | 8.24 | 0.457 |
Two findings the benchmark is designed to expose: forecasting skill concentrates in motion-active, maneuver-related intervals, and geometric accuracy and behavioral-state anticipation favor different model families — coordinates alone don't tell you whether a hand is about to move.
🤖 Can models actually predict driver motion?
Watch a forecaster work on anchored pre-maneuver windows — the model sees 8 s of motion history (plus road context) and rolls out 4 s into the future:
Left: ground truth (blue trails = nose/wrists). Right: Transformer (+ctx, enriched) prediction (orange trails) over the faint ground-truth ghost.
Stochastic models produce diverse futures on the same observation:
🗂️ Repository layout
DriveMotion/
├── assets/ # card figures & GIFs
├── motion/ # ⬆ uploading — npz + meta.json per sequence
│ ├── baton/ ├── web/ └── aide/
├── render/ # ⬆ uploading — skeleton motion videos (*.motion.mp4)
├── events/ # CAN maneuver event bank (per-route parquet)
├── benchmark/ # manifest, splits, anchored windows, thresholds,
│ # fixed subsets, exterior-context features, caches
├── code/ # loader + full benchmark reference implementation
└── demo/ # runnable demo script + sample sequences
🚧 Upload in progress — the full data payload (~330 GB) is being pushed in stages. The card, demos, and benchmark definitions land first; motion tensors and skeleton videos follow.
🔒 Privacy & license
- The released visual modality is skeleton motion video — behavior is preserved while appearance-based identity is substantially reduced. No identity-preserving crops of drivers are distributed for the web corpus.
- Every web curation decision is logged: the funnel ledger (per-stage rejection causes and gate values) ships with the dataset, and takedown requests are honored.
- Skeleton-derived artifacts, annotations, metadata, and code produced by DriveMotion: CC BY 4.0. Source-derived artifacts retain the licensing terms of their respective sources; aligned context-view clips are released separately under a research-only license.
- Splits are grouped by recording device / creator channel so no driver crosses a split boundary.
📖 Citation
A technical report describing DriveMotion is in preparation — citation information will be added here.
✉️ Contact
Open a discussion on this repository for questions, issues, or takedown requests.