This book constitutes the refereed proceedings of the 39th International Conference on Current Trends in Theory and Practice of computerscience, SOFSEM 2013, held in Špindlerův Mlýn, Czech Republic, in Janu...
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ISBN:
(数字)9783642358432
ISBN:
(纸本)9783642358425
This book constitutes the refereed proceedings of the 39th International Conference on Current Trends in Theory and Practice of computerscience, SOFSEM 2013, held in Špindlerův Mlýn, Czech Republic, in January 2013. The 37 revised full papers presented in this volume were carefully reviewed and selected from 98 submissions. The book also contains 10 invited talks, 5 of which are in full-paper length. The contributions are organized in topical sections named: foundations of computerscience; software and Web engineering; data, information, and knowledge engineering; and social computing and human factors.
Trajectory prediction is a crucial challenge in autonomous vehicle motion planning and decision-making techniques. However, existing methods face limitations in accurately capturing vehicle dynamics and interactions. ...
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Trajectory prediction is a crucial challenge in autonomous vehicle motion planning and decision-making techniques. However, existing methods face limitations in accurately capturing vehicle dynamics and interactions. To address this issue, this paper proposes a novel approach to extracting vehicle velocity and acceleration, enabling the learning of vehicle dynamics and encoding them as auxiliary information. The VDI-LSTM model is designed, incorporating graph convolution and attention mechanisms to capture vehicle interactions using trajectory data and dynamic information. Specifically, a dynamics encoder is designed to capture the dynamic information, a dynamic graph is employed to represent vehicle interactions, and an attention mechanism is introduced to enhance the performance of LSTM and graph convolution. To demonstrate the effectiveness of our model, extensive experiments are conducted, including comparisons with several baselines and ablation studies on real-world highway datasets. Experimental results show that VDI-LSTM outperforms other baselines compared, which obtains a 3% improvement on the average RMSE indicator over the five prediction steps.
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