研究变压器和长期短期记忆神经网络汽车追踪模型,考虑到数据丢失
Pinpin Qin1, Xing Li1, Shenglin Bin1
1School of Mechanical Engineering, Guangxi University, Nanning 530004, China.
Mathematical biosciences and engineering : MBE
|December 5, 2023
概括
本研究介绍了一个LSTM-变压器模型来重建丢失的汽车跟踪特征. 该模型准确地预测了车辆的行为,并重现了交通现象,超过了现有的方法.
科学领域:
- 运输工程 运输工程
- 人工智能的人工智能
- 交通流理论的流量理论.
背景情况:
- 关于重建丢失的汽车追踪特征的研究有限.
- 了解汽车跟踪动态对于交通流分析和模拟至关重要.
研究的目的:
- 提出和评估一种新的汽车追踪模型,用于重建丢失的功能.
- 为了利用长期短期存储器 (LSTM) 和变压器模型的优势进行增强的特征重建.
- 将拟议模型的性能与现有的LSTM和智能驾驶员模型进行比较.
主要方法:
- 开发了一种集成LSTM和变压器架构的汽车追踪模型.
- 利用自然驾驶和NGSIM数据集中的700个汽车追踪段进行培训和测试.
- 将LSTM-变压器模型与独立的LSTM和智能驾驶员模型进行了比较.
主要成果:
- 该LSTM-变压器模型在汽车后续特征重建中表现出了卓越的性能.
- 该模型准确地预测了失去特征的追随车辆的位置和速度.
- 它有效地捕捉和复制复杂的交通现象,如振荡和不对称的驾驶行为.
结论:
- 拟议的LSTM-Transformer汽车后续模型提供了卓越的特征重建能力.
- 这个模型准确地复制了交通现象,超过了传统的LSTM和智能驾驶员模型.
- 该研究强调了混合深度学习模型在交通行为分析中的潜力.
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