VaLW: multi-centric motion forecasting with variable length window

Heng Qin

doi:10.1117/12.3034981

7 August 2024 VaLW: multi-centric motion forecasting with variable length window

Heng Qin

Proceedings Volume 13224, 4th International Conference on Internet of Things and Smart City (IoTSC 2024); 1322409 (2024) https://doi.org/10.1117/12.3034981
Event: 4th International Conference on Internet of Things and Smart City, 2024, Hangzhou, China

Abstract

Trajectory prediction plays a crucial role in achieving autonomous driving, as it significantly reduces driving risks by predicting the movement trajectory of other vehicles. The key challenge lies in effectively encoding scene information and generating accurate multimodal results for each agent. To address this challenge, we propose a graph neural network framework that enables multi-centric modeling of relationships between heterogeneous inputs. This framework establishes various spatiotemporal adjacency relationships among scene nodes, leveraging graph attention mechanisms to allow each scene node to learn neighborhood features effectively and generate scene context enriched with valuable information. To tackle the problem of declining prediction accuracy as the prediction time increases, we propose a variable length window structure. The structure consists of a long window prediction module for multi-agent multimodal prediction, followed by a short window optimization module for refining the predictions. By utilizing this structure, we successfully strike a balance between model size and prediction accuracy. To validate our proposed model, we conducted experiments on the Argverse 1 motion forecasting dataset, and the results showcased excellent predictive performance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Heng Qin "VaLW: multi-centric motion forecasting with variable length window", Proc. SPIE 13224, 4th International Conference on Internet of Things and Smart City (IoTSC 2024), 1322409 (7 August 2024); https://doi.org/10.1117/12.3034981

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KEYWORDS

Windows

Motion models

Education and training

Mathematical optimization

Performance modeling

Ablation

Autonomous driving

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