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Spatiotemporal Multivehicle Interaction Graph Modeling for Proactive Lane-Changing Risk Level Prediction in a Connected Environment
https://orcid.org/0000-0002-7841-302X
Timely and accurate prediction of lane-changing (LC) risk is crucial for drivers to make safe LC decisions. This study proposes a spatiotemporal attention graph neural network model (STAG) based on multivehicle interaction graph modeling to characterize the dynamic relationships among vehicles in a connected environment and predict upcoming LC risks. Specifically, graph theory is employed to model the interactions among a LC vehicle and its surrounding vehicles. A deep learning model combining a graph attention network (GAT), gated recurrent unit (GRU), and attention mechanism is proposed to extract the spatiotemporal features of multivehicle interaction graphs for LC risk prediction. The proposed method was validated using the highD data set. The results show that (1) compared with traditional feature input methods, using multivehicle interaction graphs can improve LC risk prediction accuracy by 1.5%; and (2) the STAG model accurately extracts the spatiotemporal features of multivehicle interaction graphs. The average accuracy of LC risk prediction was 4.4% higher than that of baseline models. The findings of this study provide valuable insights for traffic safety management and the design of advanced driver assistance systems (ADAS).
Spatiotemporal Multivehicle Interaction Graph Modeling for Proactive Lane-Changing Risk Level Prediction in a Connected Environment
https://orcid.org/0000-0002-7841-302X
Timely and accurate prediction of lane-changing (LC) risk is crucial for drivers to make safe LC decisions. This study proposes a spatiotemporal attention graph neural network model (STAG) based on multivehicle interaction graph modeling to characterize the dynamic relationships among vehicles in a connected environment and predict upcoming LC risks. Specifically, graph theory is employed to model the interactions among a LC vehicle and its surrounding vehicles. A deep learning model combining a graph attention network (GAT), gated recurrent unit (GRU), and attention mechanism is proposed to extract the spatiotemporal features of multivehicle interaction graphs for LC risk prediction. The proposed method was validated using the highD data set. The results show that (1) compared with traditional feature input methods, using multivehicle interaction graphs can improve LC risk prediction accuracy by 1.5%; and (2) the STAG model accurately extracts the spatiotemporal features of multivehicle interaction graphs. The average accuracy of LC risk prediction was 4.4% higher than that of baseline models. The findings of this study provide valuable insights for traffic safety management and the design of advanced driver assistance systems (ADAS).
Spatiotemporal Multivehicle Interaction Graph Modeling for Proactive Lane-Changing Risk Level Prediction in a Connected Environment
https://orcid.org/0000-0002-7841-302X
Timely and accurate prediction of lane-changing (LC) risk is crucial for drivers to make safe LC decisions. This study proposes a spatiotemporal attention graph neural network model (STAG) based on multivehicle interaction graph modeling to characterize the dynamic relationships among vehicles in a connected environment and predict upcoming LC risks. Specifically, graph theory is employed to model the interactions among a LC vehicle and its surrounding vehicles. A deep learning model combining a graph attention network (GAT), gated recurrent unit (GRU), and attention mechanism is proposed to extract the spatiotemporal features of multivehicle interaction graphs for LC risk prediction. The proposed method was validated using the highD data set. The results show that (1) compared with traditional feature input methods, using multivehicle interaction graphs can improve LC risk prediction accuracy by 1.5%; and (2) the STAG model accurately extracts the spatiotemporal features of multivehicle interaction graphs. The average accuracy of LC risk prediction was 4.4% higher than that of baseline models. The findings of this study provide valuable insights for traffic safety management and the design of advanced driver assistance systems (ADAS).
Spatiotemporal Multivehicle Interaction Graph Modeling for Proactive Lane-Changing Risk Level Prediction in a Connected Environment
J. Transp. Eng., Part A: Systems
Chen, Yanyan (author) / Lu, Kaiming (author) / Zhang, Yunchao (author) / Li, Yongxing (author) / Gu, Xin (author)
2025-03-01
Article (Journal)
Electronic Resource
English
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