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• W2956351969 abstract "Understanding the dynamics of an environment, such as the movement of humans and vehicles, is crucial for agents to achieve long-term autonomy in urban environments. This requires the development of methods to capture the multi-modal and probabilistic nature of motion patterns. We present Kernel Trajectory Maps (KTM) to capture the trajectories of movement in an environment. KTMs leverage the expressiveness of kernels from non-parametric modelling by projecting input trajectories onto a set of representative trajectories, to condition on a sequence of observed waypoint coordinates, and predict a multi-modal distribution over possible future trajectories. The output is a mixture of continuous stochastic processes, where each realisation is a continuous functional trajectory, which can be queried at arbitrarily fine time steps." @default.
• W2956351969 created "2019-07-23" @default.
• W2956351969 creator A5033725695 @default.
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• W2956351969 date "2019-07-11" @default.
• W2956351969 modified "2023-09-27" @default.
• W2956351969 title "Kernel Trajectory Maps for Multi-Modal Probabilistic Motion Prediction" @default.
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• W2956351969 hasPublicationYear "2019" @default.
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