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Trajectory optimization is a method for solving motion planning problems by formulating them as non-convex constrained optimization problems. The optimization process, however, can get stuck in local optima that are in collision. As a consequence, these methods typically require multiple initializations. This poses the problem of deciding which initializations to use when given a limited computational...
This paper proposes a new self-growing Bayesian network classifier for online learning of human motion patterns (HMPs) in dynamically changing environments. The proposed classifier is designed to represent HMP classes based on a set of historical trajectories labeled by unsupervised clustering. It then assigns HMP class labels to current trajectories. Parameters of the proposed classifier are recalculated...
This paper proposes an improved multi-level pedestrian behavior prediction method based on our previous research work on learning pedestrian motion patterns and predicting pedestrian long-term behaviors as their motion instances are being observed. The improvement mainly focuses on the similarity matching criteria between the trajectory and the clustered MP whose main advantages are that (1) a reasonable...
This paper proposes a behavior prediction method for navigation application in dynamically changing environments, which predicts obstacle behaviors based on learned obstacle motion patterns (OMP) from observed obstacle motion trajectories. A multi-level prediction model is then proposed that predicts long-term or short-term obstacle behaviors. Simulation results show that it works well in a complex...
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