Abstract
In the development of autonomous driving systems, pedestrian trajectory prediction plays a crucial role. Existing models still face some challenges in capturing the accuracy of complex pedestrian actions in different environments and in handling large-scale data and real-time prediction efficiency. To address this, we have designed a novel Complex Gated Recurrent Unit (CGRU) model, cleverly combining the spatial expressiveness of complex numbers with the efficiency of Gated Recurrent Unit networks to establish a lightweight model. Moreover, we have incorporated a social force model to further develop a Social Complex Gated Recurrent Unit (S-CGRU) model specifically for predicting pedestrian trajectories. To improve computational efficiency, we conducted an in-depth study of the pedestrian’s attention field of view in different environments to optimize the amount of information processed and increase training efficiency. Experimental verification on six public datasets confirms that S-CGRU model significantly outperforms other baseline models not only in prediction accuracy but also in computational efficiency, validating the practical value of our model in pedestrian trajectory prediction.
Z. Xu—First author.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China (61562082), the Joint Funds of the National Natural Science Foundation of China (U1603262), and the “Intelligent Information R &D Cross-disciplinary Project” (Project Number: 202104140010). We thank all anonymous commenters for their constructive comments.
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Xu, Z., Yu, Q., Slamu, W., Zhou, Y., Liu, Z. (2024). S-CGRU: An Efficient Model for Pedestrian Trajectory Prediction. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1964. Springer, Singapore. https://doi.org/10.1007/978-981-99-8141-0_19
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