ProIn: Learning to Predict Trajectory Based on Progressive Interactions
  for Autonomous Driving
Yinke Dong Haifeng Yuan Hongkun Liu Wei Jing Fangzhen Li Hongmin Liu Bin Fan
Accurate motion prediction of pedestrians, cyclists, and other surrounding
vehicles (all called agents) is very important for autonomous driving. Most
existing works capture map information through an one-stage interaction with
map by vector-based attention, to provide map constraints for social
interaction and multi-modal differentiation. However, these methods have to
encode all required map rules into the focal agent's feature, so as to retain
all possible intentions' paths while at the meantime to adapt to potential
social interaction. In this work, a progressive interaction network is proposed
to enable the agent's feature to progressively focus on relevant maps, in order
to better learn agents' feature representation capturing the relevant map
constraints. The network progressively encode the complex influence of map
constraints into the agent's feature through graph convolutions at the
following three stages: after historical trajectory encoder, after social
interaction, and after multi-modal differentiation. In addition, a weight
allocation mechanism is proposed for multi-modal training, so that each mode
can obtain learning opportunities from a single-mode ground truth. Experiments
have validated the superiority of progressive interactions to the existing
one-stage interaction, and demonstrate the effectiveness of each component.
Encouraging results were obtained in the challenging benchmarks.