Design of an interactive multiple model based two-stage multi-vehicle tracking algorithm for autonomous navigation

Information regarding vehicles in neighboring lanes is essential to an autonomous vehicle for decision-making during lane-change maneuvers. Complete autonomy requires effective velocity estimation of the neighboring vehicles under different road scenarios. A two-stage Interactive-Multiple-Model-based (IMM) estimator has been proposed to perform multiple target-tracking with application to vehicles in a lane-changing scenario. The first stage deals with an adaptive-window-based turn-rate estimation for tracking maneuvering targets. The estimator can detect abrupt changes in turnrates and function independently, and avoids the problem of non-linear vehicle dynamics, thereby facilitating the use of standard Kalman filter. Variable-structure models with updated estimated turn-rate are utilized in the second stage to perform data association followed by IMM-based velocity estimation. The proposed algorithm results in root-mean-squared error of position and velocity to 5-6 cm and 0.25-0.3 m/s, respectively, and the turn-rate converges up to 10% accuracy within 3-4 s. The algorithm has been validated using simulations and experimentation using mobile robots in a simulated lane environment.