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A Support Vector Machine-Based Truck Discretionary Lane Changing Decision Model

Aiming at the discretionary lane changing decision- making of trucks, this paper presents a support vector machine-based lane changing decision model. In this paper, the space headways and relative longitudinal speeds between the surrounding vehicles both in the original lane and the target lane and the ego truck, which take into account the curved roads, are extracted as features. The starting time of lane changing is determined by the truck's lateral deviation, which is considered as the lane changing time, meanwhile a certain time before the starting time is determined as the non-lane changing time. For parameter optimization the grid search is used as the coarse search and the genetic algorithm as the fine search. The accuracy on the training set and test set reached 99.6% and 96. 7% respectively, indicating that the model has a good classification performance and strong capability of generalization.