在联网车辆环境下,与车辆之间的动态空间相互作用进行多模式轨迹预测
Lisheng Jin1, Xingchen Liu1, Yinlin Wang1
1Yanshan University, Qinhuangdao, 066000, China.
Scientific reports
|February 4, 2024
概括
这项研究介绍了STA-LSTM,这是一个用于预测连接环境中的车道变化的新模型. 它准确地预测车辆路径和相互作用,在复杂的交通场景中提高安全性.
科学领域:
- 智能运输系统 智能运输系统
- 计算机视觉和模式识别
- 机器学习和人工智能的人工智能
背景情况:
- 互联汽车环境引入了复杂的交互和大量的数据输入,压倒了传统的轨迹预测模型.
- 现有的车型难以处理动态,交互式变车道的场景,因为它们只依赖目标车辆的历史数据.
- 需要稳定,有针对性的改变车道的行为预测方法,以适应连接车辆的特性.
研究的目的:
- 提出一种多模式轨迹预测模型 (STA-LSTM) 用于分析互联车辆变车道场景中的交互行为.
- 通过结合动态空间相互作用和扩展多模式特征输入来增强轨迹预测.
- 为了提高复杂的交互式交通中车道更改预测的准确性和稳定性.
主要方法:
- 开发了STA-LSTM,这是一个多模式轨迹预测模型,集成空间网格占用率用于车辆交互建模.
- 引入了一个空间维度的注意力机制,以适应地权衡周围车辆对目标车辆的影响.
- 将注意模块纳入LSTM解码器 (时间维度),以识别重要的历史特征和背景信息,以便进行可靠的预测.
主要成果:
- 与基线模型相比,STA-LSTM模型显示了较低的根平均平方误差 (RMSE) 和负日志概率 (NLL).
- 在预测时间分别为1s,2s,3s,4s和5s时,获得了0.46m,1.15m,1.89m,2.84m和4.05m的RMSE值.
- 该模型准确地预测了车辆互动和周围车辆在互联环境中的行驶路径.
结论:
- 在连接的车辆中,STA-LSTM有效地处理复杂的交互式变车道场景.
- 该模型的注意力机制和交互建模提高了预测的准确性和稳定性.
- 这种方法为先进的驾驶辅助系统和自动驾驶提供了更可靠的轨迹预测方法.
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