Lane-Changing Intention Recognition Based on Multivehicle Interaction Dynamic Graph Modeling in a Connected Environment
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 150, Issue 6
Abstract
Accurate and proactive lane-changing (LC) intention recognition can assist drivers in making LC decisions to improve driving safety. However, the mechanism of drivers’ LC decisions in dynamically changing environments is still not fully understood, which makes it difficult for advanced driver-assistance systems (ADASs) to make accurate LC decisions under different working conditions. To accurately capture the dynamic features before the generation of LC intention, relying on the multivehicle interaction capability of the connected environment, a LC intention recognition framework using graph theory to model the interaction relationship among multiple vehicles, i.e., the Multivehicle Interaction Dynamic Time Graph (MIDTG) framework, is proposed. First, the interaction relationship between LC vehicles and their surrounding vehicles in the connected communication range is modeled by graph theory. Second, the graph convolutional network (GCN) is used to extract spatial features of multivehicle interactions, and a long short-term memory (LSTM) neural network is used to learn the association of multivehicle interaction graphs in a time series. Finally, LC intentions are output through the Softmax function. The highD data set is used to validate the proposed model. Results show that the model can accurately extract the dynamic features of multivehicle interactions within a time window of 2.5 to 3.5 s, and the accuracy of LC intention recognition reaches 98%, which is an average improvement of 3.5% compared with other baseline models. The study provides a new way to model multivehicle interactions in the connected environment, which can be helpful for ADASs’ LC decision-making.
Practical Applications
The significance of this study is to provide guidance for advanced driver-assistance systems (ADASs) to better assist drivers in making lane-changing decisions. In this study, a novel multivehicle interaction modeling method based on graph theory is proposed, combined with an advanced deep learning method to extract the spatiotemporal characteristics of multivehicle interaction, and finally applied to lane-changing intention recognition. The results show that the proposed method achieves good lane-changing intention recognition accuracy and can accurately extract key information from multivehicle interactions. This will provide a new way to predict driving behavior in a connected environment. This study will be meaningful for traffic operators and can spur a new wave of innovative applications in ADASs, such as lane-changing alerts.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions.
Acknowledgments
The study was funded by the National Natural Science Foundation of China (62203021) and the National Key Research and Development Program of China (2I038001201902).
Author contributions: Yunchao Zhang: conceptualization, methodology, software, data curation; Yanyan Chen: conceptualization; Yongxing Li: writing–reviewing and editing; Jianling Huang: writing–reviewing and editing; Siyang Li: editing.
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Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 150 • Issue 6 • June 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Sep 6, 2023
Accepted: Jan 19, 2024
Published online: Apr 13, 2024
Published in print: Jun 1, 2024
Discussion open until: Sep 13, 2024
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Business management
- Computer programming
- Computing in civil engineering
- Driver behavior
- Dynamic models
- Employment
- Engineering fundamentals
- Highway transportation
- Infrastructure
- Model accuracy
- Models (by type)
- Neural networks
- Personnel management
- Practice and Profession
- Traffic engineering
- Traffic management
- Traffic safety
- Transportation engineering
- Vehicles
- Working conditions
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