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20 December 2024 Attention based feature extraction and fusion for autonomous vehicle trajectory prediction
Longyun Liu, Ying Xia
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Proceedings Volume 13421, Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024); 134214I (2024) https://doi.org/10.1117/12.3054601
Event: Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), 2024, Dalian, China
Abstract
Vehicle trajectory prediction is a key technology in advancing autonomous driving and intelligent transportation systems, which is of great significance to improve traffic safety and efficiency. This paper addresses the challenges posed by complex traffic scenarios and vehicle interactions by proposing ABFEF, a precise and dependable vehicle trajectory prediction model. The feature extraction network captures multi-scale interactions between vehicles and between vehicles and the surrounding environment, while the feature fusion network further integrates global semantic information to enhance prediction accuracy and robustness. We evaluate ABFEF on Argoverse 1, a benchmark in motion forecasting. Our experiments demonstrate that ABFEF outperforms other models in terms of prediction accuracy and reliability. Experimental results validate the effectiveness of our approach in improving autonomous vehicle trajectory prediction under complex traffic conditions.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Longyun Liu and Ying Xia "Attention based feature extraction and fusion for autonomous vehicle trajectory prediction", Proc. SPIE 13421, Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), 134214I (20 December 2024); https://doi.org/10.1117/12.3054601
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KEYWORDS
Feature extraction
Feature fusion
Autonomous driving
Unmanned vehicles
Autonomous vehicles
Deep learning
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