Source: Deep Learning on Medium
The Proposed Solution: Using Anchors for Hierarchical Prediction
Anchors: Coarse trajectory predictions that are pre-computed using unsupervised methods such as clustering or uniform sampling. Use these to reduce a lot of the learning effort from the model, eliminate the model collapse issue (since now the ‘diverse’ trajectories are precomputed), and introduce hierarchy to the prediction process.
Hierarchy here is achieved by the model first reasoning about the intent (like U-turn, left turn etc.) by assigning likelihoods to the fixed number of pre-computed anchor trajectories. Say there are three anchor trajectories (corresponding to intents of LEFT turn, RIGHT turn, STRAIGHT), then one plausible likelihood assignment is 0.4, 0.1, 0.5 to these respectively.
Then, for each intent, it produces control uncertainty at each time step by outputting a mean value that corresponds to the offset from anchor state, and associated covariance that captures the aleatoric uncertainty on this offset.
Note that this concept of anchors is similar to the one used by Fast RCNN, where first the model predicts likelihoods over anchors, and then continuos refinements on top like box corner location offsets.