| --- |
| 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 |
| --- |
| |
| <div align="center"> |
|
|
| # 🚗 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 |
|
|
| <img src="assets/teaser.jpg" width="92%"/> |
|
|
| </div> |
|
|
| --- |
|
|
| ## 🎬 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) | |
| |:---:|:---:|:---:| |
| | <img src="assets/demo_baton.gif" width="240"/> | <img src="assets/demo_web.gif" width="240"/> | <img src="assets/demo_aide.gif" width="240"/> | |
| | 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**: |
|
|
| <div align="center"><img src="assets/data_example.jpg" width="92%"/></div> |
|
|
| | 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: |
|
|
| <div align="center"><img src="assets/pipeline.jpg" width="92%"/></div> |
|
|
| <div align="center"><img src="assets/stats.png" width="92%"/></div> |
|
|
| --- |
|
|
| ## 📦 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. |
|
|
| ```python |
| 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): |
|
|
| <div align="center"><img src="assets/protocol.png" width="92%"/></div> |
|
|
| - **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: |
|
|
| <div align="center"> |
| <img src="assets/pred_vs_gt_0.gif" width="80%"/> |
| <br/> |
| <img src="assets/pred_vs_gt_1.gif" width="80%"/> |
| <br/> |
| <sub>Left: ground truth (blue trails = nose/wrists). Right: Transformer (+ctx, enriched) |
| prediction (orange trails) over the faint ground-truth ghost.</sub> |
| </div> |
|
|
| Stochastic models produce diverse futures on the same observation: |
|
|
| <div align="center"><img src="assets/ddpm_diversity.jpg" width="80%"/></div> |
|
|
| --- |
|
|
| ## 🗂️ 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. |
|
|
