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A manoeuvre indicator and ensemble learning-based risky driver recognition approach for highway merging areas
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A manoeuvre indicator and ensemble learning-based risky driver recognition approach for highway merging areas
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Journal Article
A manoeuvre indicator and ensemble learning-based risky driver recognition approach for highway merging areas
2024
Overview
Due to the complex traffic characteristics in highway merging areas, drivers tend to exhibit high-risk driving behaviours. To address the characteristics of driving behaviour in highway merging areas, we have developed a real-time identification model for risky drivers by combining a driver risk level labelling method with load balancing-ensemble learning (LB-EL). In this paper, we explore four types of manoeuvre indicator indexes (MIIs)—acute direction, stomp pedal, dangerous following and dangerous lane changing—that can describe the negative behaviours of both individual vehicles and vehicle platoons in highway merging areas. To quantize the label driver risk level, we use the interquartile range (IQR) method and Criteria Importance Though Intercriteria Correlation (CRITIC) while evaluating the reliability of the MII using spatial analysis. Furthermore, we balance the dataset using three load balancing (LB) algorithms and create nine ensemble strategies by pairing adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost) and light gradient boosting machine (LGBM) with the three LB algorithms. Finally, we validate the proposed model using trajectory data extracted from unmanned aerial vehicle (UAV) videos. The results indicate that the distribution laws of risky driving behaviours in the acute direction and stomp pedal show a high degree of similarity and good matching with the distribution laws of traffic conflict points in existing research. Moreover, the synthetic minority over-sampling technique-light gradient boosting machine (SMOTE-LGBM) ensemble model achieves the best performance, reaching an accuracy rate of 93.4%, and a recall rate of 92.1%, which demonstrates the validity of our proposed model. This model can be widely applied to recognize risky drivers in video-based surveillance systems.
Publisher
Oxford University Press
