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

Abstract

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.

Privacy Preserving Human Mobility Generation using Grid based Data and Graph Autoencoders

“…Local Potential City Wide Socio-Demographic Travel Demand Generation [32][33][34] LSTM [22][23][24][25] LSTM [26] GAN [14] [26-28] Pairwise Reorganization [29] Many data-driven approaches are developed, especially for generating general trajectories, without including personal information or biases between groups. These approaches are scalable, from generating pedestrian trajectories [22] to city-wide traffic counts [14].…”

“…Local Potential City Wide Socio-Demographic Travel Demand Generation [32][33][34] LSTM [22][23][24][25] LSTM [26] GAN [14] [26-28] Pairwise Reorganization [29] Many data-driven approaches are developed, especially for generating general trajectories, without including personal information or biases between groups. These approaches are scalable, from generating pedestrian trajectories [22] to city-wide traffic counts [14]. These approaches to generate pedestrian trajectories consider both temporal and spatial relations and a social aspect, like the interaction between different pedestrians, bicycles, and traffic members in the local area [23].…”

A Pedestrian Trajectory Prediction Method for Generative Adversarial Networks Based on Scene Constraints

“…Based on the above methods, Amirian et al [10] added an attention mechanism to the network to screen the miscellaneous pedestrian interaction information, reducing the computational load of the network and improving the prediction efficiency. PEI Zhao et al [11] proposed a transformer generative adversarial network (GAN) algorithm, which combines dynamic scene information with pedestrian social interaction information. The convolution neural network model of the dynamic scene extraction module is utilized to extract the dynamic scene information features of the target pedestrian, which improves the average error and the final displacement error.…”