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Multi-modal anticipation of stochastic trajectories in a dynamic environment with Conditional Variational Autoencoders
Forecasting short-term motion of nearby vehicles presents an inherently challenging issue as the space of their possible future movements is not strictly limited to a set of single trajectories.
Exploiting latent representation of sparse semantic layers for improved short-term motion prediction with Capsule Networks
As urban environments manifest high levels of complexity it is of vital importance that safety systems embedded within autonomous vehicles (AVs) are able to accurately anticipate short-term future motion of nearby agents.
Physically constrained short-term vehicle trajectory forecasting with naive semantic maps
Urban environments manifest a high level of complexity, and therefore it is of vital importance for safety systems embedded within autonomous vehicles (AVs) to be able to accurately predict the short-term future motion of nearby agents.
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