重庆大学大数据与软件学院
TP391???????
1.CHONGQING CHANGAN AUTOMOBILE Co., Ltd;2.School of Big Data &3.Software Engineering, Chongqing University
针对传统的舒适性评估方法依赖于主观问卷难以满足实时性和客观性需求的问题，提出了一种融合变分推断和AutoFormer+的舒适性预测方法，实现对驾驶人舒适度的实时、客观的评估。基于变分推断的舒适性预测方法以脑电信号和坐姿关节角为输入，坐姿舒适度主观评分为标签，AutoFormer+算法通过引入频域增强模块，对注意力模块进行优化，完成对驾驶人脑电数据的舒适度分类。该方法在实车静态测试数据集上进行了实验，所提出的算法在舒适度分类任务上取得了优于传统方法的性能，精确率达0.95，较基线模型提升0.5%。
In order to solve the problem that the traditional comfort evaluation method relies on subjective questionnaires to meet the needs of real-time and objectivity, a comfort prediction method integrating variational inference and AutoFormer+ was proposed to realize the real-time and objective evaluation of driver"s comfort. The comfort prediction method based on variational inference takes the EEG signal and sitting joint angle as inputs, and the subjective score of sitting comfort as the label, and the AutoFormer+ algorithm optimizes the attention module by introducing the frequency domain enhancement module to complete the comfort classification of the driver"s EEG data. In this paper, experiments are carried out on the static test dataset of real vehicles, and the proposed algorithm achieves better performance than the traditional method in the comfort classification task, with an accuracy of 0.95, which is 0.5% higher than that of the baseline model.
