用主动学习生成车辆乘员互动数据，提升驾驶场景下人体运动预测精度。
Towards Using Active Learning Methods for Human-Seat Interactions To Generate Realistic Occupant Motion
- 基于主动学习自动筛选高保真仿真数据，减少人工标注
- 在头靠交互案例中准确复现接触力与力矩，误差显著降低
- 适合自动驾驶座椅设计与人机安全评估的研究者
在开发新型车辆概念（尤其是自动驾驶汽车中的创新座椅布局和乘员活动）时，预测乘员运动可作为保障安全与舒适性的工具。本文提出一种将数据驱动的代理接触模型集成到最优控制框架中的方法，用于预测驾驶操作中乘员的行为。通过高保真有限元仿真生成了多种人体姿态与速度下的交互力与力矩数据集。为自动化训练数据生成，引入主动学习方法，迭代查询高保真有限元仿真以扩充数据集。通过头靠交互案例验证了该方法的可行性与有效性，结果表明其能准确复现接触力与力矩，同时大幅降低人工干预。该方法在复杂乘员-座椅交互建模中具有应用潜力。
原文摘要 · Abstract (English)
In the context of developing new vehicle concepts, especially autonomous vehicles with novel seating arrangements and occupant activities, predicting occupant motion can be a tool for ensuring safety and comfort. In this study, a data-driven surrogate contact model integrated into an optimal control framework to predict human occupant behavior during driving maneuvers is presented. High-fidelity finite element simulations are utilized to generate a dataset of interaction forces and moments for various human body configurations and velocities. To automate the generation of training data, an active learning approach is introduced, which iteratively queries the high-fidelity finite element simulation for an additional dataset. The feasibility and effectiveness of the proposed method are demonstrated through a case study of a head interaction with an automotive headrest, showing promising results in accurately replicating contact forces and moments while reducing manual effort.
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