Multimodal Forward Generation Transformer Network for Inconspicuous Pedestrian Trajectory Prediction
- 8 January 2024
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Robotics and Automation Letters
- Vol. 9 (3) , 2224-2231
Abstract
Pedestrian's future trajectory prediction is a key challenge in ego-centric view of autonomous driving system. Most of the current methods are flawed in capturing subtle change features in a lightweight model size. To solve this problem, we propose a multimodal forward generation transformer network based on encoder-decoder structure. Different from the traditional transformer, we improve layer normalization and propose frame normalization, which can more successfully capture minute time-variant properties. In addition, we believe that considering short-term pedestrian's future goals can help the ego-vehicle to predict more accurate and reasonable long-term pedestrians’ trajectory. Therefore, based on the idea of forward generation, the decoder considers the future short-term targets and uses trajectory-time correlation module to capture the relationship between estimated short-term future goals and global spatial-temporal context cues of the historical trajectory. Our model is evaluated on JAAD and PIE datasets and achieves state-of-the-art performance while maintaining a lightweight model size.Keywords
Funding Information
- Key Discipline Project of Guangzhou Xinhua University (2020XZD02)
- Guangdong Key Discipline Scientific Research Capability Improvement Project (2021ZDJS144, 2022ZDJS151)
This publication has 24 references indexed in Scilit:
- Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction2020
- Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction2020
- TITAN: Future Forecast Using Action Priors2020
- Multiple Object Forecasting: Predicting Future Object Locations in Diverse Environments2020
- PIE: A Large-Scale Dataset and Models for Pedestrian Intention Estimation and Trajectory Prediction2019
- Forecasting Pedestrian Trajectory with Machine-Annotated Training Data2019
- Long-Term On-board Prediction of People in Traffic Scenes Under Uncertainty2018
- Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks2018
- Are They Going to Cross? A Benchmark Dataset and Baseline for Pedestrian Crosswalk Behavior2017
- A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction2017