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AttSCNs:一个贝叶斯优化的混合模型,带有注意力引导的随机配置网络,用于稳健的GPS轨迹预测
Xue-Bo Jin1, Ye-Qing Wang1, Jian-Lei Kong1
1School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China.
Entropy (Basel, Switzerland)
|November 26, 2025
概括
本研究介绍了AttSCNs,这是一种用于汽车互联网 (IoV) 轨迹预测的新型框架. 它有效地处理GPS噪音和长期依赖,改善道路安全和交通流动.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 轨迹预测对于汽车互联网 (IoV) 的安全性和效率至关重要.
- 现有的方法与GPS噪声和长期轨迹依赖性作斗争.
研究的目的:
- 开发一个概率混合框架,AttSCNs,用于强大的IoV轨迹预测.
- 为应对有色噪声和长期依赖GPS数据的挑战.
主要方法:
- 随机配置网络 (SCN) 与基于注意力的编码器的集成.
- 利用SCN的随机神经元进行适应性噪声过.
- 采用贝叶斯式超参数优化来实现强大的模型配置.
主要成果:
- 在现实世界GPS数据集上,AttSCN显著优于传统方法.
- 与SCN相比,实现了36.51%的RMSE减少和97.8%的MAE减少与卡尔曼过器相比.
- 证明了52.5%的RMSE和68.5%的MAE减少与LSTM相比,具有实时推断.
结论:
- AttSCNs为IoV轨迹预测提供了一个强大的,耐噪声的解决方案.
- 该框架提高了自动驾驶和智能城市系统的性能.
- 量化预测不确定性,以提高可靠性.
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