🧬 DriveDNA-Sample

A small, directly browsable slice of DriveDNA. Every drive is one folder holding one concatenated forward-view mp4 and one decoded csv on the same clock — no manifests to join, no segment files to stitch.

63 drives · 56.2 h · 6.55 GB video · 0.55 GB signals · 2,048,504 rows · 14 car models

Driver pseudonyms (driver_078, drive_004, …) are identical to those in the full DriveDNA release, so anything you find here can be traced straight back to the main dataset's splits, embeddings, and index tables.

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Why these drivers

The sample is not a random subset. It is built around the two comparisons that DriveDNA's benchmark is designed to separate — the same person in different cars versus different people in the same car.

🚗 One driver, many vehicles — driver_078

driver_078 logged 14 different car models, from a Toyota Camry to a Tesla Model X to a VW Tiguan. Their behavioural signature has to be read through fourteen different vehicle platforms, each with its own steering geometry, powertrain response, and CAN signal set. This is the axis that isolates driver identity from vehicle dynamics.

Car model	Drives	Hours
HONDA_ACCORD_HYBRID_2018	4	3.18
HONDA_CIVIC	1	0.60
HYUNDAI_IONIQ_5	4	4.96
HYUNDAI_IONIQ_5_2022	4	3.56
KIA_EV6	4	1.31
KIA_NIRO_EV_2ND_GEN	4	1.38
KiaNiro2023	4	6.59
TESLA_AP3_MODEL_3	4	3.08
TESLA_MODEL_X	4	3.11
TOYOTA_CAMRY_2021	4	4.48
TOYOTA_CAMRY_TSS2	4	4.39
TOYOTA_RAV4_2023	4	1.83
TOYOTA_RAV4_TSS2_2023	4	4.24
VOLKSWAGEN_TIGUAN_MK2	4	3.88
Total	53	46.59

👥 Many drivers, one vehicle — the shared HONDA_CIVIC

Four different people drove the same Honda Civic. Vehicle platform is held fixed, so whatever separates these drives is the person behind the wheel, not the car.

Driver	Drives	Hours	CSV schema
driver_174	5	6.90	drivedna_v1
driver_369	1	1.00	drivedna_v1
driver_372	1	1.05	drivedna_v1
driver_427	3	0.70	legacy_gnss_removed
Total	10	9.65

driver_078 also appears once on the Civic (HONDA_CIVIC/driver_078), so the two groups overlap on a single shared platform.

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📁 Layout

Dataset/<CAR_MODEL>/<driver_XXX>/<drive_YYY>/
                                 ├── drive_YYY.mp4    # concatenated forward video
                                 └── drive_YYY.csv    # decoded signals, same clock
index.csv                                             # one row per drive

Dataset/TESLA_MODEL_X/driver_078/drive_021/drive_021.mp4
Dataset/TESLA_MODEL_X/driver_078/drive_021/drive_021.csv
Dataset/HONDA_CIVIC/driver_372/drive_001/drive_001.mp4
Dataset/HONDA_CIVIC/driver_372/drive_001/drive_001.csv

drive_YYY numbering is per driver, exactly as in the full release: driver_078's drives run drive_001…drive_240 across all fourteen of their car models.

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🎬 Video

Each .mp4 is the drive's ~60-second qcamera segments concatenated in order.

Codec	H.264, stream-copied — not re-encoded, bit-identical to source
Resolution	526 × 330
Frame rate	20 fps
Audio	removed (-an)
Container	MP4, +faststart

ffmpeg -f concat -safe 0 -fflags +genpts -i segments.txt \
       -c copy -an -movflags +faststart drive_YYY.mp4

In 12 of the 63 files the final frame is truncated and decoders emit one error while decoding MB … warning at the very end. This comes from the source recordings — the logger stopped mid-frame — and is present in the original segments as well; stream copying preserves it rather than introducing it. Everything before the last frame decodes cleanly.

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📊 Signals

drivedna_v1 — 27 columns @ 10 Hz (60 of 63 drives)

Identical to raw_signals_csv/<driver_XXX>/<drive_YYY>.csv in the full DriveDNA release.

Group	Columns
Time	time_s
Ego motion	vEgo, aEgo, vEgoCluster, yawRate, yaw_rate
Steering	steeringAngleDeg, steeringRateDeg, steeringPressed, actual_curvature, curv_measured, slip
Pedals	gas, gasPressed, brake, brakePressed
Lead vehicle	leadOne_status, leadOne_dRel, leadOne_vLead, leadOne_vRel
Lane	laneLeft_y, laneRight_y
Indicators	leftBlinker, rightBlinker
Automation	cruiseState_enabled (OEM ACC), cs_enabled (openpilot)
Human mask	is_human

is_human is the column that matters. ADAS-engaged frames are kept in place rather than cut out, so the timeline stays continuous against the video. Filter to is_human == 1 for human-controlled driving:

import pandas as pd
d = pd.read_csv("Dataset/TESLA_MODEL_X/driver_078/drive_021/drive_021.csv")
human = d[d.is_human == 1]          # video time = d.time_s, unchanged

legacy_gnss_removed — 3 columns @ 20 Hz (driver_427, 3 drives)

driver_427's Civic drives predate the current pipeline and no raw logs survive for them, only a pre-rendered mp4 and a thin CSV. They are included so the shared-Civic group is complete, but they carry t, yaw_ned, vEgo only — no CAN channels, no is_human mask. The original lat / lon / alt columns were stripped, matching DriveDNA's policy of releasing no GNSS coordinates.

These three drives (driver_427/drive_003…005) exist only in this sample — the full DriveDNA release covers driver_427 through their two Ford Mustang Mach-E drives, which do have raw logs. Check csv_schema in index.csv before assuming columns exist.

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🗂️ index.csv

One row per drive: group, car_model, driver, drive, n_video_segments, video_s, csv_rows, csv_cols, csv_s, csv_hz, csv_schema, mp4_MB, csv_MB.

ix = pd.read_csv("index.csv")
ix[ix.group == "one_driver_many_vehicles"].car_model.nunique()   # 14

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⏱️ Clock alignment

Video and CSV both start at drive time zero, so CSV row i sits at i / csv_hz seconds into the mp4. Recording occasionally stops a moment later on one stream than the other: the median csv_s − video_s gap is 0.27 s, and the largest is 75.9 s (one trailing segment logged after the camera stopped). Use video_s and csv_s in index.csv when exact end-of-drive behaviour matters.

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🔐 Privacy

• Drivers appear only as driver_XXX; raw device identifiers are not distributed.
• No GPS. No coordinates in any schema; the legacy CSV had its GNSS columns removed.
• No audio. Microphone tracks present in some source recordings are dropped.
• Forward-view video only — no cabin or driver-facing camera.
• Original collection files were never modified in producing this release.

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📄 Full dataset & citation

The complete corpus — 465 drivers, 115 car models, 4,121 drives, 975 h, plus frozen video embeddings, benchmark splits, maneuver annotations, and the evaluation harness — lives at HenryYHW/DriveDNA.

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