Research on Vehicle-Driving-Trajectory Prediction Methods by Considering Driving Intention and Driving Style

Liming Shao, Meining Ling, Ying Yan, Guangnian Xiao, Shiqi Luo and Qiang Luo ()
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Liming Shao: Guangzhou Urban Planning & Design Survey Research Institute Co., Ltd., Guangzhou 510060, China
Meining Ling: Guangzhou Urban Planning & Design Survey Research Institute Co., Ltd., Guangzhou 510060, China
Ying Yan: School of Automobile, Key Laboratory of Automobile Transportation Safety Support Technology, Chang’an University, Xi’an 710064, China
Guangnian Xiao: School of Economics and Management, Shanghai Maritime University, Shanghai 201306, China
Shiqi Luo: School of Civil Engineering and Transportation, Guangzhou University, Guangzhou 510006, China
Qiang Luo: School of Civil Engineering and Transportation, Guangzhou University, Guangzhou 510006, China

Sustainability, 2024, vol. 16, issue 19, 1-15

Abstract: With the rapid advancement of autonomous driving technology, the accurate prediction of vehicle trajectories has become a research hotspot. In order to accurately predict vehicles’ trajectory, this study comprehensively explores the impact of driving style and intention on trajectory prediction, proposing a novel prediction method. Firstly, the dataset AD4CHE was selected as the research data, from which the required trajectory data of vehicles were extracted, including 1202 lane-changing and 1137 car-following driving trajectories. Secondly, a long short-term memory (LSTM) network based on the Keras framework was constructed by using the TensorFlow deep-learning platform. The LSTM network integrates driving intention, driving style, and historical trajectory data as inputs to establish a vehicle-trajectory prediction model. Finally, the mean absolute error (MAE) and root-mean-square error (RMSE) were selected as the evaluation indicators for the models, and the prediction results of the models were compared under two conditions: not considering driving style and considering driving style. The results demonstrate that models incorporating driving style significantly outperformed those that did not, highlighting the critical influence of driving style on vehicle trajectories. Moreover, compared to traditional kinematic models, the LSTM-based approach exhibits notable advantages in long-term trajectory prediction. The prediction method that accounts for both driving intention and style effectively reduces RMSE, significantly enhancing prediction accuracy. The findings of this research provide valuable insights for vehicle-driving risk assessment and contribute positively to the advancement of autonomous driving technology and the sustainable development of road traffic.

Keywords: traffic safety; trajectory prediction; driving intention and style; sustainable transportation; long short-term memory (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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