According to the producing mechanism of driver's lane change decision, desired speed satisfaction, risk perception coefficient and change feasibility coefficient are put forward and quantified as the identification parameters of lane change decision. The results of analyzing real vehicle test data indicate that quantitative indicators have different correlation with lane change decision, and there is a significant difference among the beginning of lane changing, lane keeping and transition state stage. Fuzzy neural network model is established to identify driver's lane change intention by using desired speed satisfaction, risk perception coefficient and feasibility coefficient of lane change as the input feature index. The research results show that the model accuracy in the early stage of lane change is 89.93%, and the false alarm rate is 9.52%, which both are better than BP neural network model by taking the collision time TTC as input vectors and the Logistic model by using RV, RP and RS as variables. It shows that the model has a good predictive accuracy.%依据驾驶人换道决策的产生机制，提出速度期望满足度、危险感知系数和换道可行性系数作为换道决策的识别指标并确定其量化方法.通过实车试验数据的分析表明：量化指标与换道决策存在不同程度的相关性；同时在换道初期、车道保持及过渡状态阶段存在显著差异.以速度期望满足度、危险感知系数和换道可行性系数为特征输入参数，建立基于模糊神经网络的驾驶人换道意图识别模型，进行驾驶人换道意图的识别.结果表明，该模型在换道初期的预测准确率达到89.93%，虚警率为9.52%，优于以碰撞时间TTC为输入指标的BP神经网络模型，以及以RV、RP、RS为变量的Logistic模型，说明模型具有较好的预测准确性.
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