Identification of cut-in maneuver of side lane vehicles based on fuzzy support vector machines

For detecting the driver's cut-in intention of a side lane vehicle, a cut-in intention identifier is built using machine learning technique based on fuzzy support vector machines (FSVM). The training samples of identifier obtained from the cut-in data of vehicles in real traffic environment possess 7 motion attributes of vehicles in both main lane and side lane, in which the attributes not able to get directly from sensors are pre-estimated by Kalman filter. Due to the cut-in samples cannot be effectively distinguished from non cut-in samples at the initial moment of cut-in, a fuzzy membership coefficient is introduced for each sample in solving FSVM to improve the training accuracy of cut-in identifier, and a grid optimization is conducted on the parameters of FSVM aiming at the highest correctness rate of cross validation. The results of the test on cut-in intention identifier in real traffic environment show that the identifier is effective in work with identification results effectively reflect the cut-in intention of driver after simple processing.