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Journal Article
SPU-BERT: Faster human multi-trajectory prediction from socio-physical understanding of BERT
- Authors
- Ki-In Na, Ue-Hwan Kim, Jong-Hwan Kim
- Issue Date
- 2023-08
- Citation
- Knowledge-Based Systems, v.274, pp.1-13
- ISSN
- 0950-7051
- Publisher
- Elsevier BV
- Language
- English
- Type
- Journal Article
- Abstract
- Accurately predicting pedestrian trajectories requires a human-like socio-physical understanding of movement, nearby pedestrians, and obstacles. However, traditional methods struggle to generate multiple trajectories in the same situation based on socio-physical understanding and are computationally intensive, making real-time application difficult. To overcome these limitations, we propose SPU-BERT, a fast multi-trajectory prediction model that incorporates two non-recursive BERTs for multi-goal prediction (MGP) and trajectory-to-goal prediction (TGP). First, MGP predicts multiple goals through generative models, followed by TGP generating trajectories that approach the predicted goals. SPU-BERT can simultaneously understand movement, social interaction, and scene context from trajectories and semantic maps using a single Transformer encoder, providing explainable results as evidence of socio-physical understanding. In experiments, SPU-BERT accurately predicted future trajectories (with 0.19 m and 7.54 pixels of ADE20 for the ETH/UCY datasets and SDD) with over 100 times faster computation (0.132 s) than the state-of-the-art method. The code is available at https://github.com/kina4147/SPUBERT.
- KSP Keywords
- Generative models, Human-like, Real-Time applications, Traditional methods, multiple trajectories, non-recursive, prediction model, scene context, semantic map, social interaction, state-of-The-Art
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
