Lane Change Prediction With an Echo State Network and Recurrent Neural Network in the Urban Area

The prediction of lane changes can reduce traffic accidents and improve traffic flow. In this paper two classifiers, Echo State Network and a recurrent neural network with Long Short Term Memory cells, were compared to predict lane changes using the input variables steering angle and indicator. The input variables were extracted from a data set which was generated from a naturalistic driving study in the urban area of Chemnitz, Germany. Both classifiers predicted left and right lane changes successfully. They achieved high true positive rates and low false positive rates. The Echo State Network predicted left and the recurrent neural network predicted right lane changes better.