Lane change prediction can reduce traffic accidents and improve traffic flow. To predict lane changes variables which describe lane changes are needed. Recent studies used different classifiers and different inputs for lane change classification and prediction. Here, different methods are used to extract the relevant input variables from a data set which was generated from a naturalistic driving study in the urban area of Chemnitz, Germany. First variables which show different characteristics for left and no lane changes were chosen. The variables contained driver attributes (for instance gazes), environment attributes (for instance distance to other vehicles) and vehicle attributes (for instance velocity). Second, different combinations of these input variables were analyzed with the principal component analysis. In the end, the best combinations were used to classify left lane changes with an Echo State Network and a feedforward neural network. The Echo State Network achieved high area under the curve values, true positive rates and low false positive rates for the classification with a majority of the input combinations. The feedforward neural network predictions were inferior of those to the Echo State Network.

车道变化预测可以减少交通事故并改善交通流量。为了预测车道变化，需要描述车道变化的变量。最近的研究使用不同的分类器和不同的输入进行车道变更分类和预测。在这里，使用不同的方法从数据集中提取相关的输入变量，该数据集是根据德国开姆尼茨市区的自然驾驶研究得出的。首先选择对左侧显示不同特征且不改变车道的变量。这些变量包含驾驶员属性（例如凝视），环境属性（例如与其他车辆的距离）和车辆属性（例如速度）。其次，使用主成分分析法分析了这些输入变量的不同组合。到底，最佳组合用于通过回声状态网络和前馈神经网络对左车道变化进行分类。对于大多数输入组合，分类的回波状态网络在曲线值下实现了较高的面积，真阳性率和假阳性率低。前馈神经网络的预测要比回声状态网络的预测低。