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In complex traffic environment, it is still a great challenge for Autonomous Vehicles (AVs) to understand the surrounding drivers&#x2019; Lane-Changing Decision (LCD) intention accurately. The LCD intention is affected by Driver&#x2019;s Psychology (DP) and Driving Style (DS). But few LCD studies considered DP and DS simultaneously. We previously proposed a LCD model, by combing DP and DS, termed as DP&#x0026;DS-LCD model. Nevertheless, there are some factors not fully considered in this model, including the driver&#x2019;s Visual Attention (VA) in DP quantification and the influence of driving state on DS. Therefore, an enhanced LCD Model is developed, by integrating the DP under VA (DPVA) and DS Layering (DSL), named as DPVA&#x0026;DSL-LCD model. In the model, a psychological field model coupling driver&#x2019;s VA mechanism is established to represent the surrounding vehicles&#x2019; influence on the driver. Then, a DSL framework is proposed by adding the influence of driving state on DS. The Gaussian Mixture Model (GMM) clustering and Support Vector Machine (SVM) classifier are respectively adopted in training and recognition phases to identify the current driving style. Finally, integrating the DPVA and DSL, the Light Gradient Boosting Machine (LightGBM) algorithm is used to train the LCD model. In experiments, the open I-80 database from Next Generation Simulation (NGSIM) is adopted to train the DPVA&#x0026;DSL-LCD. And compared with other three LCD models, the prediction performance of DPVA&#x0026;DSL-LCD model achieved the best. Therefore, the DPVA&#x0026;DSL-LCD model is effective and could provide support for the decision-making of AVs by predicting surrounding vehicles&#x2019; LCD intention.

Lane-Changing Decision Intention Prediction of Surrounding Drivers for Intelligent Driving