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.

翻译：人类行为的多模态性与随机性使得运动预测成为极具挑战性的任务，这对自动驾驶至关重要。尽管深度学习方法已在该领域展现出巨大潜力，但如何建立多驾驶场景（例如并线、环岛、交叉路口）与深度学习模型设计之间的关联仍悬而未决。当前基于学习的方法通常采用统一模型预测不同场景下的轨迹，这可能导致单一场景下的次优结果。为解决此问题，我们提出多场景网络（简称MS-Net），这是一种通过进化过程训练的多路径稀疏模型。MS-Net在推理阶段选择性激活部分参数以生成各场景的预测结果。在训练阶段，差异化场景下的运动预测任务被抽象为多任务学习问题，通过设计进化算法激励网络为每个场景搜索最优参数，同时保持不同场景间的共性知识共享。实验结果表明，在参数显著减少的情况下，MS-Net在ETH、UCY等成熟的行人运动预测数据集上优于现有最优方法，并在INTERACTION挑战赛中位列第二。