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XGBoost-DNN Mixed Model for Predicting Driver’s Estimation on the Relative Motion States during Lane-Changing Decisions: A Real Driving Study on the Highway

Author

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  • Chen Zhao

    (School of Automobile, Chang’an University, Xi’an 710064, China)

  • Xia Zhao

    (School of Automobile, Chang’an University, Xi’an 710064, China)

  • Zhao Li

    (School of Automobile, Chang’an University, Xi’an 710064, China)

  • Qiong Zhang

    (China Communications Press Co., Ltd., Beijing 100101, China)

Abstract

This study is conducted on a real live highway to investigate the driver’s performance in estimating the speed and distance of vehicles behind the target lane during lane changes. Data on the participants’ estimated and actual data on the rear car were collected in the experiment. Ridge regression is used to analyze the effects of both the driver’s features, as well as the relative and absolute motion characteristics between the target vehicle and the subject vehicle, on the driver’s estimation outcomes. Finally, a mixed algorithm of extreme gradient boosting (XGBoost) and deep neural network (DNN) was proposed in this paper for establishing driver’s speed estimation and distance prediction models. Compared with other machine learning models, the XGBoost-DNN prediction model performs more accurate prediction performance in both classification scenarios. It is worth mentioning that the XGBoost-DNN mixed model exhibits a prediction accuracy approximately two percentage points higher than that of the XGBoost model. In the two-classification scenarios, the accuracy estimations of XGBoost-DNN speed and distance prediction models are 91.03% and 92.46%, respectively. In the three-classification scenarios, the accuracy estimations of XGBoost-DNN speed and distance prediction models are 87.18% and 87.59%, respectively. This study can provide a theoretical basis for the development of warning rules for lane-change warning systems as well as insights for understanding lane-change decision failures.

Suggested Citation

  • Chen Zhao & Xia Zhao & Zhao Li & Qiong Zhang, 2022. "XGBoost-DNN Mixed Model for Predicting Driver’s Estimation on the Relative Motion States during Lane-Changing Decisions: A Real Driving Study on the Highway," Sustainability, MDPI, vol. 14(11), pages 1-23, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:11:p:6829-:d:830881
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    References listed on IDEAS

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    1. Huiting Zheng & Jiabin Yuan & Long Chen, 2017. "Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation," Energies, MDPI, vol. 10(8), pages 1-20, August.
    2. Robert J. Snowden & Nicola Stimpson & Roy A. Ruddle, 1998. "Speed perception fogs up as visibility drops," Nature, Nature, vol. 392(6675), pages 450-450, April.
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