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2 results on '"Liang, Deyuan"'

1. Enhancing Interaction Modeling with Agent Selection and Physical Coefficient for Trajectory Prediction

Author

Huang, Shiji, Ye, Lei, Chen, Min, Luo, Wenhai, Wang, Dihong, Xu, Chenqi, and Liang, Deyuan

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

A thorough understanding of the interaction between the target agent and surrounding agents is a prerequisite for accurate trajectory prediction. Although many methods have been explored, they all assign correlation coefficients to surrounding agents in a purely learning-based manner. In this study, we present ASPILin, which manually selects interacting agents and calculates their correlations instead of attention scores. Surprisingly, these simple modifications can significantly improve prediction performance and substantially reduce computational costs. Additionally, ASPILin models the interacting agents at each past time step separately, rather than only modeling the interacting agents at the current time step. This clarifies the causal chain of the target agent's historical trajectory and helps the model better understand dynamic interactions. We intentionally simplified our model in other aspects, such as map encoding. Remarkably, experiments conducted on the INTERACTION, highD, and CitySim datasets demonstrate that our method is efficient and straightforward, outperforming other state-of-the-art methods., Comment: code:https://github.com/kkk00714/ASPILin

Published
2024

2. Enhancing Interaction Modeling with Agent Selection and Physical Methods for Trajectory Prediction

Author

Huang, Shiji, Ye, Lei, Chen, Min, Luo, Wenhai, Xu, Chenqi, Liang, Deyuan, Wang, Dihong, Huang, Shiji, Ye, Lei, Chen, Min, Luo, Wenhai, Xu, Chenqi, Liang, Deyuan, and Wang, Dihong

Abstract

In this study, we address the limitations inherent in most existing vehicle trajectory prediction methodologies that indiscriminately incorporate all agents within a predetermined proximity when accounting for inter-agent interactions. These approaches commonly employ attention-based architecture or graph neural networks for encoding interactions, which introduces three challenges: (i) The indiscriminate selection of all nearby agents substantially escalates the computational demands of the model, particularly in those interaction-rich scenarios. (ii) Moreover, the simplistic feature extraction of current time agents falls short of adequately capturing the nuanced dynamics of interactions. (iii) Compounded by the inherently low interpretability of attention mechanism and graph neural networks, there is a propensity for the model to allocate unreliable correlation coefficients to certain agents, adversely impacting the accuracy of trajectory predictions. To mitigate these issues, we introduce ASPILin, a novel approach that enhances the selection of interacting agents by considering their current and future lanes, extending this consideration across all historical frames. Utilizing the states of the agents, we estimate the nearest future distance between agents and the time needed to reach this distance. Then, combine these with their current distances to derive a physical correlation coefficient to encode interactions. Experiments conducted on popular trajectory prediction datasets demonstrate that our method is efficient and straightforward, outperforming other state-of-the-art methods., Comment: code:https://github.com/kkk00714/ASPILin

Published
2024

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