Datasets:

HenryYHW
/

DriveDNA

pretty_name: DriveDNA
license: other
license_name: drivedna-research-license
license_link: LICENSE
language:
  - en
task_categories:
  - time-series-forecasting
  - other
tags:
  - naturalistic-driving
  - driving-style
  - driver-identification
  - autonomous-driving
  - CAN-bus
  - multimodal
  - time-series
  - benchmark
  - shortcut-learning
size_categories:
  - 10K<n<100K
arxiv: '2607.23822'

🧬 DriveDNA

A Large-Scale Multimodal Naturalistic Driving Dataset and Benchmark for Driving Style Identification

Recognizing a driver is not the same as capturing driving style — DriveDNA makes vehicle, route, and driving-condition shortcuts measurable.

📌 TL;DR

DriveDNA turns a large, in-the-wild naturalistic driving corpus into a benchmark for personalized driving style: representing who is driving as distinct from what they are driving and where. It pairs time-synchronized CAN telemetry (10 Hz) and forward-road video across hundreds of drivers and vehicle models, retains only human-controlled driving (automation-engaged frames removed), and ships a frozen evaluation protocol whose central question is:

Does a model recognize how a person drives — or merely which car they own, which roads they frequent, and which conditions they encounter?

Why it's unique. Public personalized-style resources are small and hold vehicle/route fixed (e.g., PDB: 12 drivers, one car), while large AV datasets (nuScenes, Waymo) carry no persistent driver identity. DriveDNA is the first public corpus combining many drivers × many vehicles × multi-session CAN+video, with clean human-vs-automation separation and explicit confound diagnostics.

✨ Highlights


🧑‍✈️ Drivers	465 persistent, salted-hashed identities, consistent across vehicles
🚗 Vehicle models	115 across 26 brands — 392 drivers share a model with another driver; 22 drivers appear on 2+ models
🛣️ Drives	4,121 decoded drives (Mar 2023 – Jul 2026, multi-continent)
⏱️ Human-controlled driving	975 h total, 581 h in motion, at 10 Hz with forward video
🪟 Benchmark windows	62,674 tagged 60-s windows from 428 drivers (355 in frozen folds)
🏷️ Annotations	6 driving scenarios · 8 behavioral primitives (93.0% audit agreement) · 276,248 maneuver events incl. 22,322 individually verified lane changes
🧪 Protocol	Driver-disjoint splits · 3-seed error bars · frozen evaluation manifests · leakage probes

📡 Modalities & Committed Signals

All streams are decoded from openpilot logs and resampled to a unified 10 Hz grid:

Signal	Meaning	Style construct
`vEgo`, `aEgo` (+ jerk)	speed, longitudinal accel	longitudinal aggressiveness
`steeringAngleDeg`, `steeringRateDeg`	driver steering INPUT (vehicle-dependent via steer ratio)	steering entropy, reversal rate
`actual_curvature`	realized path curvature (vehicle-normalized)	cornering sharpness, path geometry
`yaw_rate` → `curv_measured`	independently-sensed turning	aggressiveness, slip
`leadOne_dRel/vRel/status`	lead-vehicle distance & relative speed (radar)	THW, TTC, gap preference
`gas`, `brake` (+ pressed)	pedal application (subset of fleet)	pedal dynamics
`laneLeft_y`, `laneRight_y`	lane offsets	lane-keeping (SDLP)

Key distinction — steering INPUT vs realized PATH. steeringAngleDeg is the raw wheel input and is vehicle-dependent; actual_curvature is the vehicle-normalized realized path. Their gap is a signal-level handle on the "who vs which-car" question at the heart of the benchmark: vehicle-model probes read 2.3× chance from steering angle but ≈chance from realized curvature.

🎯 Benchmark Tasks & Splits

Task	Input → Output	Metrics
Driver re-identification (core)	k-min support → driver identity	Top-k, AUROC, EER
Personalized behavior prediction (core)	5-s history → 1–5-s future motion	RMSE, PG, MMD/KL/W1
Condition-matched comparison (core)	matched window pair → same driver?	AUROC, EER
Event forecasting (optional)	5-s history → event in 1–5 s	AP, AUROC, lead time
Style explanation (optional)	event window → category + evidence	accuracy (exploratory)

The main driver-disjoint split is 212 train / 45 val / 45 test, plus a 53-driver few-shot hold-out (support and query always from different drives). Additional frozen manifests isolate generalization sources: within-nameplate (same model, different drivers, 24 models), cross-vehicle (same driver, different vehicles), condition-matched pairs (14,868), and missing-channel robustness.

📊 Key Results

Finding	Evidence
Learned representations ≫ classical descriptors	AUROC .935 vs .707 on unseen drivers
Driver signal survives condition matching	.811 ± .006 on 14,868 matched pairs (descriptors → .550, chance)
High re-ID ≠ driving style	Video-only probe hits .937 re-ID but predicts route at 347× chance; collapses to .675 under matching
Recognition ≠ prediction	Best re-ID embedding yields no prediction gain (−0.2%); task-aligned FiLM conditioning does (+0.4 to +1.4%)
Foundation models need adaptation	Zero-shot LLM/TS/VLM rows land at/below the descriptor level; 1-epoch LoRA lifts Qwen3-8B to .871 on event forecasting

30 baseline configurations across five families — representation learning, shortcut robustness, personalization, multimodal modeling, distributional prediction — under one fixed multi-seed protocol.

📦 What's Released

Tier	Contents
Public (this repo)	De-identified 10 Hz signal tables · frozen video embeddings (DINOv2/DINOv3/SigLIP2/V-JEPA 2) · all split manifests · VLM scene attributes · evaluation harness · baseline training code
Gated (DUA)	Raw forward video (faces/plates blurred), research use only

The public tier alone reproduces every number in the paper.

Planned public-tier layout:

DriveDNA/
├── data/
│   ├── segments/windows.parquet        # 62,674 windows: driver, model, scenario, primitives, stats
│   ├── segments/windows_x.npy          # [62674, 600, 17] 10 Hz CAN windows
│   ├── segments/maneuver_events.parquet# 276,248 events (6 classes, verified lane changes flagged)
│   ├── splits/                         # driver_folds / within_vehicle / cross_vehicle / matched pairs
│   └── embeddings/                     # frozen DINOv2 / DINOv3 / SigLIP2 / V-JEPA 2 features
├── code/
│   ├── eval/harness.py                 # enrollment protocol, metrics, distribution distances, leakage probes
│   └── model/                          # all 30 baseline configurations
└── README.md

🔒 Ethics & Privacy

Collected from community drivers with informed consent and compensation; follows source-platform terms.
Driver identifiers are salted hashes; VINs, device identifiers, and GPS coordinates removed; no cabin video/audio; faces and plates blurred in the gated video tier.
Leakage probes ship as part of the benchmark — users are asked to report leakage alongside utility.
Prohibited: re-identification attempts; insurance, employment, or law-enforcement scoring of individuals.
A takedown contact allows any driver to request removal from future versions.

📖 Citation

@article{drivedna2026,
  title   = {DriveDNA: A Large-Scale Multimodal Naturalistic Driving Dataset and
             Benchmark for Driving Style Identification},
  author  = {Wang, Yuhang and Li, Lingyao and Zhou, Hao},
  journal = {arXiv preprint arXiv:2607.23822},
  year    = {2026}
}

🔗 Links & Status

📄 Paper: arXiv:2607.23822 · 🤗 Papers page · KDD 2027 Datasets & Benchmarks (under review)
💻 Code & harness: github.com/WangYuHang-cmd/DriveDNA
📦 Data files: uploading in stages — signal tables and manifests first, embeddings next
✉️ Contact: haozhou1@usf.edu

_{DriveDNA · University of South Florida & University of Arizona · 2026}