Changchang Che, Shici Luo, Wangyang Zong, Yuli Zhang, Helong Wang
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引用次数: 0
Abstract
To enhance the perception of vehicle trajectory information and lane-changing decision-making capabilities in intelligent connected vehicles during multivehicle interaction scenarios, we propose a novel method based on a Multimodal Adversarial Informer (MAI) for highway multivehicle lane-changingrajectory prediction. This method achieves spatiotemporal features of target and surrounding vehicles through graph learning of temporal features and spatial adjacency matrices. Considering the heading angle and vehicle local X-axis displacement, the vehicle trajectory samples are categorized for training and validation of the multimodal Informer. A multi-criterion discriminator is utilized to judge whether the generated trajectory fits the requirements of accuracy and rationality. After adversarial learning, the optimal vehicle lane-changing trajectory prediction is obtained using the proposed MAI. Experiments conducted with the NGSIM dataset demonstrate the comparative performance of baseline models on three different noise-added testing datasets using MAE, RMSE, and R² metrics. The MAI model consistently outperforms the others, achieving the lowest MAE and RMSE and the highest R² values across all datasets, indicating superior predictive accuracy and fit. Furthermore, the results show that the proposed MAI framework maintains a relatively low prediction error over both short-term and long-term horizons compared to baseline models.
为了增强智能网联汽车在多车交互场景中对车辆轨迹信息的感知和变道决策能力,我们提出了一种基于多模态对抗信息器(MAI)的高速公路多车变道轨迹预测新方法。该方法通过对时间特征和空间邻接矩阵的图学习,获得目标车辆和周围车辆的时空特征。考虑到航向角和车辆局部 X 轴位移,对车辆轨迹样本进行分类,以便对多模态信息器进行训练和验证。利用多标准判别器来判断生成的轨迹是否符合准确性和合理性的要求。经过对抗学习后,利用所提出的 MAI 获得最佳车辆变道轨迹预测。使用 NGSIM 数据集进行的实验表明,在三个不同的噪声添加测试数据集上,基线模型使用 MAE、RMSE 和 R² 指标进行了性能比较。MAI 模型的性能始终优于其他模型,在所有数据集上都获得了最低的 MAE 和 RMSE 值以及最高的 R² 值,表明其预测准确性和拟合度都非常出色。此外,结果表明,与基线模型相比,拟议的 MAI 框架在短期和长期范围内都能保持相对较低的预测误差。
期刊介绍:
Physica A: Statistical Mechanics and its Applications
Recognized by the European Physical Society
Physica A publishes research in the field of statistical mechanics and its applications.
Statistical mechanics sets out to explain the behaviour of macroscopic systems by studying the statistical properties of their microscopic constituents.
Applications of the techniques of statistical mechanics are widespread, and include: applications to physical systems such as solids, liquids and gases; applications to chemical and biological systems (colloids, interfaces, complex fluids, polymers and biopolymers, cell physics); and other interdisciplinary applications to for instance biological, economical and sociological systems.