Lane change decision prediction: an efficient BO-XGB modelling approach with SHAP analysis

Abstract

The lane-change decision (LCD) is a critical aspect of driving behaviour. This study proposes an LCD model based on a Bayesian optimization (BO) framework and extreme gradient boosting (XGBoost) to predict whether a vehicle should change lanes. First, an LCD point extraction method is proposed to refine the exact LCD points with a highD dataset to increase model learning accuracy. Subsequently, an efficient XGBoost with BO (BO-XGB) was used to learn the LCD principles. The prediction accuracy on the highD dataset was 99.14% with a computation time of 66.837s. The accuracy on the CQSkyEyeX dataset was 99.45%. Model explanation using the shapley additive explanation (SHAP) method was developed to analyse the mechanism of the BO-XGB’s LCD prediction results, including global and sample explanations. The former indicates the particular contribution of each feature to the model prediction throughout the entire dataset. The latter denotes each feature's contribution to a single sample.

KEYWORDS:

• Lane change decision-making
• XGBoost
• Bayesian optimization
• Model explanation
• Integrated learning
• SHAP

Acknowledgement

This research is supported by the Key Programme of the National Natural Science Foundation of China (No. 52131203), the Natural Science Foundation of Jiangsu Province (No. BK20232019), the Jiangsu Provincial Scientific Research Center of Applied Mathematics (No. BK20233002) and the SEU Innovation Capability Enhancement Plan for Doctoral Students (No. CXJH_SEU 24199).

Disclosure statement

No potential conflict of interest was reported by the author(s).