Abstract
DriveDNA operationalizes driving style as a consistent, driver-specific behavioral pattern in how a vehicle moves under similar conditions, and isolates it from vehicle identity, route, and environment confounds. It contains 4,121 drives from 465 drivers across 115 vehicle models — 975 hours of human-controlled driving at 10 Hz with forward video — plus 62,674 annotated 60-second windows and 276,248 maneuver events. The benchmark defines three tasks: few-shot driver re-identification, personalized behavior prediction, and condition-matched comparison. Learned representations outperform classical descriptors on unseen drivers (AUROC 0.935 vs. 0.707), but video-only models are vulnerable to "route leakage" — strong scores can stem from contextual shortcuts rather than genuine behavior — motivating robustness-aware evaluation with frozen splits, leakage diagnostics, and 30 baseline configurations.
BibTeX
@article{wang2026drivedna,
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}
}