发表机构
University of South Florida; Purdue University; Stanford University; University of Central Florida（南佛罗里达大学; 普渡大学; 斯坦福大学; 中佛罗里达大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
DriveMotion是一个大规模多源基准，用于连续驾驶员运动预测，通过动态锚定评估和操作丰富训练，显著提升预测性能。
AI 中文摘要
驾驶员运动可以提供关于持续行为、注意力和近期驾驶意图的线索。然而，现有的大多数以驾驶员为中心的数据集侧重于从短视频片段中识别预定义的驾驶员行为，而人体运动预测基准则主要针对车辆外部的运动。我们引入了DriveMotion，一个用于连续驾驶员运动预测的多源基准。DriveMotion包含来自360名驾驶员的393小时、10 Hz频率下的133关键点运动序列，整合了自然驾驶数据、精选的公共车内视频以及AIDE数据集，形成具有逐关节有效性掩码和同步驾驶上下文的统一表示。自然驾驶包含长时间有限的肢体运动，使得均匀采样的评估被持久性主导，对短暂但有行为意义的运动不敏感。为解决这一问题，我们采用动态锚定评估，将预测窗口放置在离线从CAN信号中识别的车辆操作周围，在推理时不向模型提供CAN。操作前窗口中的手臂运动比路线匹配的稳定驾驶对照组高3.4倍。在这些锚定窗口上，学习模型将预测误差相对于持久性降低高达15%，而操作丰富训练将预测派生的部分状态F1分数相对于零运动参考提高44%。在完整多源语料库上训练进一步将留出网络驾驶员的预测误差相对于仅BATON训练降低38%。DriveMotion提供身份不相交划分、固定评估子集和参考实现，用于连续驾驶员运动预测的可复现评估。数据集和基准可在以下网址获取：https://this https URL
英文摘要
Driver motion can provide cues to ongoing behavior, attention, and near-term driving intent. However, most existing driver-centric datasets focus on recognizing predefined driver behaviors from short video clips, while human motion forecasting benchmarks largely target motion outside the vehicle. We introduce DriveMotion, a multi-source benchmark for continuous driver motion forecasting. DriveMotion contains 393 hours of 133-keypoint motion sequences at 10 Hz from 360 drivers, integrating naturalistic driving data, curated public in-cabin videos, and the AIDE dataset into a unified representation with per-joint validity masks and synchronized driving context. Naturalistic driving contains long periods of limited body movement, making uniformly sampled evaluation dominated by persistence and less sensitive to brief but behaviorally meaningful motion. To address this, we use dynamics-anchored evaluation, placing forecasting windows around vehicle maneuvers identified offline from CAN signals without providing CAN to the model at inference. Arm motion in pre-maneuver windows is 3.4x greater than in route-matched stable-driving controls. On these anchored windows, learned models reduce forecasting error over persistence by up to 15%, while maneuver-enriched training improves forecast-derived Part-State F1 by 44% over the zero-motion reference. Training on the full multi-source corpus further reduces forecasting error on held-out web drivers by 38% compared with BATON-only training. DriveMotion provides identity-disjoint splits, fixed evaluation subsets, and reference implementations for reproducible evaluation of continuous driver motion forecasting. The dataset and benchmark are available at https://huggingface.co/datasets/HenryYHW/DriveMotion
Comments21 pages, 7 figures, 18 tables. Dataset and benchmark: https://huggingface.co/datasets/HenryYHW/DriveMotion; project page: https://wangyuhang-cmd.github.io/drivemotion/
