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In today’s automotive industry, there exist several systems that help drivers reduce the possibility of accidents, such as the ADAS (Advanced Driver Assistance System). The ADAS helps drivers make correct and quick decisions during dangerous situations. This study analyzed the performance of the IMM (Interacting Multiple Model) method based on multiple Kalman filters using the data acquired from a driving simulator. An IMM algorithm is developed to identify the current discrete state of neighboring vehicles using the sensor data and the vehicle dynamics. In particular, the driving modes of the neighboring vehicles are classified by the cruising and maneuvering modes, and the transition between the states is modeled using a Markovian switching coefficient. The performance of the IMM algorithm is analyzed through realistic simulations where a target vehicle executes sudden lane change or acceleration maneuver. Keywords: active safety system, ADAS (Advanced Driver Assistance System), vehicle tracking I.            .  1   (NHTSA: National Highway Traffic Safety Administration)             ,      [1].          25%  ,           .                ‘   (ADAS: Advanced Driver Assistance System)’  .       ‘   (LDWS: Lane Departure Warning System)’, ‘  (Blind Spot Warning)’, ‘   (ACC: Adaptive Cruise Control)’, ‘   (FCW: Forward Collision Warning)’, ‘   (Collision Avoidance System)’, ‘   (SPAS: Smart Parking Assist System)’  . ADAS  ACC FCW            .                .                         .             ,           .               .              ,               .     ,           .        , ,               . 1970 Singer         [2]  McAulay Denlinger Singer

Neighboring Vehicle Maneuver Detection using IMM Algorithm for ADAS