The multi-modality and stochastic characteristics of human behavior make
motion prediction a highly challenging task, which is critical for autonomous
driving. While deep learning approaches have demonstrated their great potential
in this area, it still remains unsolved to establish a connection between
multiple driving scenes (e.g., merging, roundabout, intersection) and the
design of deep learning models. Current learning-based methods typically use
one unified model to predict trajectories in different scenarios, which may
result in sub-optimal results for one individual scene. To address this issue,
we propose Multi-Scenes Network (aka. MS-Net), which is a multi-path sparse
model trained by an evolutionary process. MS-Net selectively activates a subset
of its parameters during the inference stage to produce prediction results for
each scene. In the training stage, the motion prediction task under
differentiated scenes is abstracted as a multi-task learning problem, an
evolutionary algorithm is designed to encourage the network search of the
optimal parameters for each scene while sharing common knowledge between
different scenes. Our experiment results show that with substantially reduced
parameters, MS-Net outperforms existing state-of-the-art methods on
well-established pedestrian motion prediction datasets, e.g., ETH and UCY, and
ranks the 2nd place on the INTERACTION challenge.

提出了一种名为 MS-Net 的多场景网络，通过进化过程训练一种多路径稀疏模型，实现对不同场景下行人运动的预测，实验结果表明，MS-Net 在行人运动预测数据集（如 ETH 和 UCY）上优于现有的最先进方法，并在 INTERACTION 挑战中排名第二。