论文标题
使用时间序列变压器的多任务驱动器转向行为建模
Multi-task Driver Steering Behaviour Modeling Using Time-Series Transformer
论文作者
论文摘要
人类意图预测为设计助手和人类驾驶员与智能车辆之间的协作提供了增强的解决方案。在这项研究中，开发了一个多任务顺序学习框架，以预测基于上肢神经肌肌电图（EMG）信号的未来转向扭矩和转向姿势。特别研究了单右手驾驶模式。对于这种驾驶模式，还评估了三种不同的驾驶姿势。然后，开发了多任务时间序列变压器网络（MTS-Trans）来预测转向扭矩和驱动姿势。为了评估多任务学习绩效，评估了四个不同的框架。 21名参与者参与了基于驾驶模拟器的实验。提出的模型实现了对未来转向扭矩预测的准确预测结果，并驱动姿势识别单手驾驶模式。拟议的系统可以有助于开发高级驾驶员转向助理系统，并确保人类驾驶员与智能车辆之间的相互了解。
Human intention prediction provides an augmented solution for the design of assistants and collaboration between the human driver and intelligent vehicles. In this study, a multi-task sequential learning framework is developed to predict future steering torques and steering postures based on the upper limb neuromuscular Electromyography (EMG) signals. A single-right-hand driving mode is particularly studied. For this driving mode, three different driving postures are also evaluated. Then, a multi-task time-series transformer network (MTS-Trans) is developed to predict the steering torques and driving postures. To evaluate the multi-task learning performance, four different frameworks are assessed. Twenty-one participants are involved in the driving simulator-based experiment. The proposed model achieved accurate prediction results on the future steering torque prediction and driving postures recognition for single-hand driving modes. The proposed system can contribute to the development of advanced driver steering assistant systems and ensure mutual understanding between human drivers and intelligent vehicles.
