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Vehicle Driving Intent Recognition Based on Enhanced Bidirectional Long Short-Term Memory Network
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Dong He, Maojie Zhao, Zinan Wang
Automotive Engineer | 2023, (9) : 9 - 14
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Automotive Engineer | 2023, (9): 9-14
• Special Topic on Autonomous Driving Technology at Chongqing Jiaotong University •
Vehicle Driving Intent Recognition Based on Enhanced Bidirectional Long Short-Term Memory Network
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Dong He, Maojie Zhao, Zinan Wang
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  • Chongqing Jiaotong University, Chongqing 400074
Published: 2023-09-15 doi: 10.20104/j.cnki.1674-6546.20230315
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In the context of high-speed mixed traffic and intricate multi-vehicle interaction, existing driving intention recognition models for research vehicles mostly neglect driving styles and vehicle-vehicle information interaction, this paper introduced a novel driving intention recognition model based on an enhanced Bidirectional Long Short-Term Memory (Bi LSTM) network, with the driving trajectory sequence of the target vehicle, driving style, and interaction features of surrounding vehicles as inputs for effective training and learning, to facilitate the classification and recognition of the driving intention feature dataset, specifically considering diverse driving styles. Additionally, the Whale Optimization Algorithm (WOA) was employed to optimize hyperparameters, encompassing the number of hidden layer nodes and learning rate, effectively mitigating the adverse impacts of manual parameter adjustment. The model’s efficacy was validated using the NGSIM dataset, exhibiting an impressive recognition accuracy of 97.5% in precisely identifying vehicle driving intentions.

Autonomous driving  /  Multi-vehicle interaction  /  Driving intention recognition  /  Bidirectional Long Short-Term Memory (Bi LSTM) network  /  Whale Optimization Algorithm (WOA)
Dong He, Maojie Zhao, Zinan Wang. Vehicle Driving Intent Recognition Based on Enhanced Bidirectional Long Short-Term Memory Network[J]. Automotive Engineer, 2023 , (9) : 9 -14 . DOI: 10.20104/j.cnki.1674-6546.20230315
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doi: 10.20104/j.cnki.1674-6546.20230315
  • Online Date：2025-11-25
  • Published：2023-09-15
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  • Revised：2023-08-07
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    Chongqing Jiaotong University, Chongqing 400074
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