Lane changing prediction at highway lane drops using support vector machine and artificial neural network classifiers

High accuracy of lane changing prediction is beneficial to driver assistant system and fully autonomous cars. This paper proposes a lane changing prediction model based on combined method of Supporting Vector Machine (SVM) and Artificial Neural Network (ANN) at highway lane drops. The vehicle trajectory data are from Next Generation Simulation (NGSIM) data set on U.S. Highway 101 and Interstate 80. The SVM and ANN classifiers are adopted to predict the feasibility and suitability to change lane under certain environmental conditions. The environment data under consideration include speed difference, vehicle gap, and the positions. Three different classifiers to predict the lane changing are compared in this paper. The best performance is the proposed combined model with 94% accuracy for non-merge behavior and 78% accuracy for merge behavior, demonstrating the effectiveness of the proposed method and superior performance compared to other methods.