Spatial-Temporal-Spectral LSTM: A Transferable Model for Pedestrian Trajectory Prediction
Artikel i vetenskaplig tidskrift, 2023

Predicting the trajectories of pedestrians is critical for developing safe advanced driver assistance systems and autonomous driving systems. Most existing models for pedestrian trajectory prediction focused on a single dataset without considering the transferability to other previously unseen datasets. This leads to poor performance on new unseen datasets and hinders leveraging off-the-shelf labeled datasets and models. In this paper, we propose a transferable model, namely the "Spatial-Temporal-Spectral (STS) LSTM" model, that represents the motion pattern of pedestrians with spatial, temporal, and spectral domain information. Quantitative results and visualizations indicate that our proposed spatial-temporal-spectral representation enables the model to learn generic motion patterns and improves the performance on both source and target datasets. We reveal the transferability of three commonly used network structures, including long short-term memory networks (LSTMs), convolutional neural networks (CNNs), and Transformers, and employ the LSTM structure with negative log-likelihood loss in our model since it has the best transferability. The proposed STS LSTM model demonstrates good prediction accuracy when transferring to target datasets without any prior knowledge, and has a faster inference speed compared to the state-of-the-art models. Our work addresses the gap in learning knowledge from source datasets and transferring it to target datasets in the field of pedestrian trajectory prediction, and enables the reuse of publicly available off-the-shelf datasets.
Marie Skłodowska-Curie Actions; Innovative Training Network (ITN); Project name: SHAPE-IT; Grant number: 860410; Publication date: 13 June 2023; DOI: 110.1109/TIV.2023.3285804

Pedestrian trajectory prediction

spectral representation

deep learning

Fourier transform

transferable models

Författare

Chi Zhang

Göteborgs universitet

Zhongjun Ni

Linköpings universitet

Christian Berger

Göteborgs universitet

IEEE Transactions on Intelligent Vehicles

23798858 (eISSN)

Supporting the interaction of Humans and Automated vehicles: Preparing for the Environment of Tomorrow (Shape-IT)

Europeiska kommissionen (EU) (EC/H2020/860410), 2019-10-01 -- 2023-09-30.

Styrkeområden

Informations- och kommunikationsteknik

Transport

Infrastruktur

ReVeRe (Research Vehicle Resource)

Ämneskategorier

Farkostteknik

Robotteknik och automation

Sannolikhetsteori och statistik

Datavetenskap (datalogi)

Datorseende och robotik (autonoma system)

DOI

10.1109/TIV.2023.3285804

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2024-08-09