To reduce the risk of lane changing behaviors, based upon drivers’ visual characteristics and vehicle motion states, a method for lane change prediction is proposed. By using visual tracking system, millimeter-wave radar and so on, the research group conducts experiments under real road environment. Based on drivers’ fixation characteristics of the rearview mirrors before lane change occurs, lane changing intent time window is determined as 5 s, the characteristic index for predict lane changing behavior is further built. By designing BP neural network, the lane change prediction model is constructed. Results show that the model may predict drivers’ lane changing behavior for at least 1.5 s in advance, and the prediction accuracy can reach 95.58%. As compared to predict lane change behavior via turn signals, the prediction accuracy and time series characteristics are both improved remarkably, thus verifying the effectiveness of the predictive index and method.