Year: 2025
Type: article
Abstract: Vehicle trajectory is crucial for autonomous driving. Relatively scattered trajectory data points pose difficulties in modeling the motion’s inherent continuity in spatial and temporal dimensions. Additionally, identifying the driving patterns of vehicles from trajectories is also a significant challenge. These implicit characteristic patterns are difficult to discern from the complex details of the trajectory data. To address these issues, we propose a new framework called Wavelet-Mamba Trajectory Prediction (WMTP), which fuses wavelet analysis through state-space modeling to capture global trends in driving patterns and details of vehicle motion. The approach employs the Discrete Wavelet Transform (DWT) to decompose trajectory data into wavelet coefficients in different time scales and frequencies, and then utilizes these coefficients to generate the trajectory through the Inverse Discrete Wavelet Transform (IDWT). An encoder-decoder neural architecture is proposed for learning potential temporal features from the input trajectory sequences, and these features are projected into the wavelet domain. Wavelet coefficients of future trajectories are generated using different scale-oriented decoders. The estimated coefficients are further used to realize the trajectory prediction via the IDWT module. Experiments demonstrate that WMTP exhibits excellent performance on three large-scale real-world trajectory prediction datasets, with promising robustness and inference speed. The research findings also verify the effectiveness of time - frequency analysis in trajectory prediction tasks.
DOI: 10.1145/3772081
Authors: Zhiyang Yin, Qingyang Xu, Yong Song, Bao Pang, Yibin Li, Ning Wang
Institutions: Shandong University, Dalian Maritime University
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4415382174