Traffic Risk Assessment of Lane-Changing Process in Urban Inter-Tunnel Weaving Segment

Zhao, Yi; Zhou, Jiayi; Zhao, Chuwei; Li, Minghao

doi:10.1177/03611981231160171

Cited by 5 publications

(1 citation statement)

References 19 publications

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“…Lane-changing behavior is a complex process influenced by various factors, including human, vehicle, road, and environmental factors [3][4][5][6][7]. Lane-change intention is defined as a driver's planned or intended action to change lanes while driving.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Machine-Learning Models for Recognizing Lane-Change Intention Using Vehicle Trajectory Data

Yuan,

Ding,

Wang

2023

Infrastructures

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Accurate detection and prediction of the lane-change (LC) processes can help autonomous vehicles better understand their surrounding environment, recognize potential safety hazards, and improve traffic safety. This study focuses on the LC process, using vehicle trajectory data to select a model for identifying vehicle LC intentions. Considering longitudinal and lateral dimensions, the information extracted from vehicle trajectory data includes the interactive effects among target and adjacent vehicles (54 indicators) as input parameters. The LC intention of the target vehicle serves as the output metric. This study compares three widely recognized machine-learning models: support vector machines (SVM), ensemble methods (EM), and long short-term memory (LSTM) networks. The ten-fold cross-validated method was used for model training and evaluation. Classification accuracy and training complexity were used as critical metrics for evaluating model performance. A total of 1023 vehicle trajectories were extracted from the CitySim dataset. The results indicate that, with an input length of 150 frames, the XGBoost and LightGBM models achieve an impressive overall classification performance of 98.4% and 98.3%, respectively. Compared to the LSTM and SVM models, the results show that the two ensemble models reduce the impact of Types I and III errors, with an improved accuracy of approximately 3.0%. Without sacrificing recognition accuracy, the LightGBM model exhibits a sixfold improvement in training efficiency compared to the XGBoost model.

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