A Recurrent Neural Network-Based Architecture for Predicting Vehicular Trajectories in Urban Environments

• Breno Krohling UFES
• Giovanni Comarela UFES
• Vinícius F. S. Mota UFES

DOI: https://doi.org/10.5753/sbrc.2024.1399

Abstract

Predicting vehicular trajectories plays a crucial role in optimizing traffic management and enhancing inter-vehicle communication. In this context, this study proposes an architecture based on stacked Long Short-Term Memory (LSTM), where the output of one LSTM serves as the input to the subsequent layer. The multiple LSTM layers enable the proposed architecture to accurately predict vehicle positions in both near and distant future. Two performance metrics have been introduced: the absolute error measured by the distance between the real and predicted positions and the model’s accuracy when predicting regions of interest. To evaluate the model, trajectory datasets from taxis in Porto, Portugal, and San Francisco, USA, were employed. The results show the architecture’s robust performance in both prediction scenarios, achieving an accuracy of 83% in predicting areas of interest in the near future.

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