Driving style identification plays a crucial role in enhancing traffic safety and reducing fuel consumption. While existing literature primarily focuses on analyzing driving styles in light-duty vehicles using simulation or questionnaire data, research on heavy-duty vehicles, despite their significant contribution to fatal accidents, remains scarce. To bridge this gap, this paper presents an attention encoder-decoder deep framework for the autonomous identification of risk driving in heavy-duty vehicles. The proposed model capitalizes on multi-source heterogeneous data collected under connected environment. High-level representations of driving style are learned through a BiLSTM-based deep architecture. Through the attention mechanism, crucial information for prediction is adaptively focused, thereby generating personalized latent semantic vectors at different time steps. The framework’s evaluation utilizes multi-source heterogeneous driving data from nearly 13,000 vehicles, conclusively demonstrating the model’s superiority over competing approaches in accurately identifying risk driving behaviors in connected heavy-duty vehicles.
Attention Encoder-Decoder Network Based Autonomous Risk Driving Identification for Connected Heavy-Duty Vehicles
Lect. Notes Electrical Eng.
International Conference on Autonomous Unmanned Systems ; 2023 ; Nanjing, China September 09, 2023 - September 11, 2023
Proceedings of 3rd 2023 International Conference on Autonomous Unmanned Systems (3rd ICAUS 2023) ; Kapitel : 6 ; 62-71
2024-04-27
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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