Prediction of Mandatory Lane Changing Behavior Using Artificial Neural Network Model

Abstract

Recently, the applications of some driver assistance systems on vehicles have reduced vehicle accidents. However, studies have shown that the number of vehicle accidents caused by improper lane-changing behavior remains at a high level. Therefore, research has been focusing on developing a lane-changing assistance system to increase the safety level of driving in traffic. Many researchers have attempted to predict lane-changing behavior, and a general trend in the study of predicting driving behavior is the greater application of computational artificial intelligence. Artificial Neural Network (ANN) is one of the artificial intelligence methods, and it is well-known for its high reliability in a variety of applications. An ANN model can mimic human thinking and behavior due to its ability to capture the complex relationship among different variables in an environment of uncertainty. In this thesis, a BP (back-propagation) Neural Network model established by two methods was developed to predict a driver’s mandatory lane-changing decisions (merge or non-merge) at an early stage by considering driving environment features as the input vectors. Vehicle trajectory data from the Next Generation Simulation (NGSIM) dataset was used for training and testing the model. The results of the proposed model indicated that the prediction accuracies in advance of a driver’s actual driving behavior with a lead time of 1s, 1.5s, and 1.8s were at 89.6%, 84.9%, 78.8% for merge events, and for non-merge events were at 92.2%, 87.5%, 81.1% respectively.