发表机构
University of South Florida; University of Arizona（南佛罗里达大学; 亚利桑那大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究引入DriveDNA数据集及基准测试用于驾驶风格识别，定义驾驶风格为车辆在相似条件下的特定行为模式，通过三个核心任务评估，对比多种基线方法得出学习表示更优，视频模型有路线泄漏问题，强调可靠评估需考虑多方面因素。
AI 中文摘要
驾驶风格反映了车辆驾驶中稳定的、特定于驾驶员的模式。然而，在自然数据中，由于驾驶员在不同车辆、道路和条件下被观察，这种信号难以分离，模型可能将特定于车辆或情况的规律误认为是特定于驾驶员的风格。我们引入了DriveDNA，这是一个用于个性化驾驶风格建模的大规模自然数据集及基准测试，包含来自465名驾驶员、115种车型的4121次驾驶，共975小时10Hz的人类控制驾驶及前方视频。DriveDNA将驾驶风格定义为车辆在相似条件下移动的一致、特定于驾驶员的行为模式。该基准测试通过三个核心任务评估此信号，并提供行为注释和大量人工审核的六类276248个规则生成的操纵事件。我们评估了多种基线方法，结果表明学习到的表示在未见过的驾驶员上显著优于经典描述符，视频模型虽有可比的重新识别准确率但存在严重路线泄漏。这些发现表明可靠的驾驶风格评估必须同时评估学习表示的行为价值及其对车辆、驾驶和条件混淆的鲁棒性。
英文摘要
Driving style captures stable, driver-specific patterns in how a vehicle is driven. In naturalistic data, however, this signal is hard to isolate because drivers are observed in different vehicles, on different roads, and under different conditions, so models may mistake vehicle- or situation-specific regularities for driver-specific style. We introduce DriveDNA, a large-scale naturalistic dataset and benchmark for personalized driving-style modeling, comprising 4,121 drives from 465 drivers across 115 vehicle models and totaling 975 hours of human-controlled driving at 10 Hz with forward video, collected from community drivers in everyday use. DriveDNA defines driving style as a consistent, driver-specific behavioral pattern in how a vehicle moves under similar conditions. The benchmark evaluates this signal through three core tasks: few-shot driver re-identification, personalized behavior prediction, and condition-matched comparison, and provides behavioral annotations plus 276,248 rule-generated maneuver events across six classes with large-scale human auditing. We evaluate baselines spanning classical descriptors, supervised and self-supervised time-series encoders, multimodal fusion, probabilistic prediction, and zero-shot foundation models under a fixed multi-seed protocol. Learned representations substantially outperform classical descriptors on unseen drivers (AUROC .935 vs. .707) and retain driver-specific information under matched driving conditions, while descriptor performance approaches chance. Video-only models achieve comparable re-identification accuracy but exhibit severe route leakage, showing that strong recognition may arise from contextual shortcuts rather than driving behavior. These findings show that reliable driving-style evaluation must assess both the behavioral value of learned representations and their robustness to vehicle, drive, and condition confounds.
