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International Journal of Automotive Technology
Erschienen in:

07.10.2024 | Connected Automated Vehicles and ITS, Vision and Sensors

Iterative Trajectory Prediction Model Based on Interactive Agent

verfasst von: Hongpeng Tian, Xiaopei Zhang, Dan Cui

Erschienen in: International Journal of Automotive Technology | Ausgabe 2/2025

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Abstract

Though many methods attempt to model how agents interact with each other and their environment, they often fail to capture the dynamic nuances of these interactions. To address this issue, this paper proposes an interactive iterative prediction model based on Transformer (IIPM-BT) that can distinguish between agents and accurately model the interactions between them. The encoder in IIPM-BT includes Map-Transformer and InterAgent-Transformer modules. The Map-Transformer module combines local maps to provide a real-time context for trajectory prediction. The InterAgent-Transformer module captures the interactive information between agents to understand the dynamic relationship between multiple agents. The decoder employs an iterative prediction strategy to refine future trajectories. This model uses the Waymo Open Motion Dataset to train and evaluate. Compared with the HDGT, our model performs significantly better in ADE, MR and MAP indicators, which are reduced by 7.02%, 21.43% and improved by 35.71% respectively. Experimental results show that the model has good performance.
Vorheriger Artikel A Novel Dynamic Lane-Changing Trajectory Planning for Autonomous Vehicles Based on Improved APF and RRT Algorithm
Nächster Artikel Multi-physical Field Coupling Analysis of Flat Wire Motor

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Metadaten
Titel
Iterative Trajectory Prediction Model Based on Interactive Agent
verfasst von
Hongpeng Tian
Xiaopei Zhang
Dan Cui
Publikationsdatum
07.10.2024
Verlag
The Korean Society of Automotive Engineers
Erschienen in
International Journal of Automotive Technology / Ausgabe 2/2025
Print ISSN: 1229-9138
Elektronische ISSN: 1976-3832
DOI
https://doi.org/10.1007/s12239-024-00154-z

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