Predicting Pedestrian Trajectories with Deep Adversarial Networks Considering Motion and Spatial Information

This paper proposes a novel prediction model termed the social and spatial attentive generative adversarial network (SSA-GAN). The SSA-GAN framework utilizes a generative approach, where the generator employs social attention mechanisms to accurately model social interactions among pedestrians. Unli...

Full description

Saved in:
Bibliographic Details
Published in:Algorithms 2023-12, Vol.16 (12), p.566
Main Authors: Lao, Liming, Du, Dangkui, Chen, Pengzhan
Format: Article
Language:English
Subjects:
Analysis
CARLA simulator
comprehensive motion feature
Datasets
Deep learning
Evaluation
Generative adversarial networks
Liquors
Methods
multi-trajectory datasets
Neural networks
pedestrian trajectory prediction
Pedestrians
Performance prediction
Prediction models
Probability distribution
Semantics
social and spatial attention
Social aspects
Social factors
Social interaction
Spatial data
Trajectory analysis
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper proposes a novel prediction model termed the social and spatial attentive generative adversarial network (SSA-GAN). The SSA-GAN framework utilizes a generative approach, where the generator employs social attention mechanisms to accurately model social interactions among pedestrians. Unlike previous methodologies, our model utilizes comprehensive motion features as query vectors, significantly enhancing predictive performance. Additionally, spatial attention is integrated to encapsulate the interactions between pedestrians and their spatial context through semantic spatial features. Moreover, we present a novel approach for generating simulated multi-trajectory datasets using the CARLA simulator. This method circumvents the limitations inherent in existing public datasets such as UCY and ETH, particularly when evaluating multi-trajectory metrics. Our experimental findings substantiate the efficacy of the proposed SSA-GAN model in capturing the nuances of pedestrian interactions and providing accurate multimodal trajectory predictions.
ISSN:1999-4893
1999-4893
DOI:10.3390/a16120566