Hierarchical Motion Encoder-Decoder Network for Trajectory Forecasting

Understand

Trajectory forecasting plays a pivotal role in the field of intelligent vehicles or social robots.

• Recent works focus on modeling spatial social impacts or temporal motion attentions, but neglect inherent properties of motions, i.e.
• moving trends and driving intentions.
• This paper proposes a context-free Hierarchical Motion Encoder-Decoder Network (HMNet) for vehicle trajectory prediction.