强大的长期车辆轨迹预测使用链接投射和局势感知变压器
Minsung Kim1, Byung Il Kwak2, Jong-Uk Hou2
1School of Computer Science and Engineering, Pusan National University, Busan 46241, Republic of Korea.
Sensors (Basel, Switzerland)
|April 27, 2024
概括
本研究引入了一种使用变压器模型的新型长期车辆轨迹预测方法. 这种方法有效地减少了预测错误,并防止了越野预测,提高了智能交通系统的准确性.
科学领域:
- 智能运输系统 智能运输系统
- 自动驾驶自动驾驶的自动驾驶
- 机器学习 机器学习
背景情况:
- 预测车辆轨迹对于智能运输系统 (ITS) 至关重要.
- 交叉路口和交通信号的城市环境使准确的长期轨迹预测变得复杂.
- 长期预测中的累积错误导致显著的不准确性和越野偏差.
研究的目的:
- 开发一种强大的长期车辆轨迹预测方法,适应错误积累.
- 为了防止预测偏离实际的道路几何.
- 引入一种用于轨迹预测准确性的新型评估指标.
主要方法:
- 利用变压器模型分析和预测车辆轨迹.
- 提出了一个额外的编码网络,以捕捉外部因素对驾驶模式的影响.
- 实施后处理"链接投影"方法,以确保预测保持在路上.
- 引入了曲线之间的面积 (ABC) 度量来评估轨迹相似性.
主要成果:
- 拟议的方法证明了对错误积累和越野预测的稳定性.
- 具有额外编码网络的变压器模型有效地捕获外部影响.
- 链接投影方法成功地将预测引导到道路几何上.
- 该ABC指标提供了更全面的评估轨迹的准确性.
结论:
- 这种新的轨迹预测方法显著优于传统的深度学习模型.
- 在真实数据集上实现了高达65.74% (RMSE),60.13% (MAE) 和91.45% (ABC) 的改进.
- 拟议的方法为复杂的城市环境中长期预测车辆轨迹提供了更准确,更可靠的解决方案.
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