The accuracy of the data is crucial to the real-time prediction of autonomous driving. Due to factors such as weather and the accuracy of data collection equipment, there frequently exist noises in the data collected in real time. Therefore, it is necessary to perform analysis on acquired kinematic features related to driving behavior prediction. This study proposes a novel deep learning framework to explore influences of data noises on lane-changing intention prediction. Kinematic features including the longitudinal distance difference, velocity and acceleration, lateral velocity and accelera...
