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基于变压器的车辆轨迹预测在城市低速T十字路口
1Department of Highway & Transportation Research, Korea Institute of Civil Engineering and Building Technology, 283 Goyangdae-ro, Goyang-si 10223, Republic of Korea.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
这项研究优化了变压器模型,用于在城市十字路口预测车辆轨迹. 具有特定输入/输出长度的轻量级模型可以提高边缘计算和事故分析的准确性.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 运输工程 运输工程
背景情况:
- 变压器模型在轨道预测方面表现出色,但需要大量的计算资源,并与长期预测作斗争.
- 车辆轨迹预测对于智能交通系统和自动驾驶至关重要,特别是在复杂的城市环境中.
研究的目的:
- 开发和优化一个轻量级的变压器模型,用于在低速的城市T-交叉点准确预测车辆轨迹.
- 确定最佳模型参数,包括损失函数和序列长度,以提高预测准确性和效率.
- 在非典型的驾驶场景中评估模型的概括能力,并评估额外功能的影响.
主要方法:
- 为培训和验证生成的微观交通模拟数据,包括非典型场景.
- 探索了各种损失函数,以平滑的L1损失为最佳性能.
- 检查了输入/输出序列长度,确定1s输入和3s输出是最佳的.
- 通过使用各种驾驶特征数据评估模型概括.
主要成果:
- 顺的L1损失函数显著提高了预测准确度.
- 确定了1秒的最佳输入序列长度和3秒的输出序列长度.
- 改进模型结构被证明比多样化训练数据在非典型情况下进行概括更有效.
- 结合额外的功能,如速度变化,将模型准确度降低了约21%.
结论:
- 优化的变压器模型为边缘计算环境中的轻量级轨迹预测提供了可行的解决方案.
- 这些发现支持开发用于各种城市驾驶场景的轨迹预测和事故分析系统.
- 专注于模型架构改进是实现在复杂和非典型的驾驶条件下强大的性能的关键.
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