Deep encoder–decoder-NN: A deep learning-based autonomous vehicle trajectory prediction and correction model
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DOI: 10.1016/j.physa.2022.126869
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References listed on IDEAS
- Jia, Dongyao & Ngoduy, Dong, 2016. "Enhanced cooperative car-following traffic model with the combination of V2V and V2I communication," Transportation Research Part B: Methodological, Elsevier, vol. 90(C), pages 172-191.
- Wang, Ke & Ma, Changxi & Qiao, Yihuan & Lu, Xijin & Hao, Weining & Dong, Sheng, 2021. "A hybrid deep learning model with 1DCNN-LSTM-Attention networks for short-term traffic flow prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
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- Wei, Cheng & Hui, Fei & Khattak, Asad J. & Zhao, Xiangmo & Jin, Shaojie, 2023. "Batch human-like trajectory generation for multi-motion-state NPC-vehicles in autonomous driving virtual simulation testing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 616(C).
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Keywords
Vehicle trajectory prediction; Multi-road section validation; Deep encoder–decoder; Attention mechanism; Network architecture optimization;All these keywords.
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