SceneMotion: From Agent-Centric Embeddings to Scene-Wide Forecasts
This work addresses motion forecasting for self-driving vehicles, which is incremental as it builds on existing attention-based models with a novel latent context module.
The paper tackles the problem of forecasting scene-wide motion modes for multiple traffic agents in self-driving contexts, achieving competitive performance in the Waymo Open Interaction Prediction Challenge.
Self-driving vehicles rely on multimodal motion forecasts to effectively interact with their environment and plan safe maneuvers. We introduce SceneMotion, an attention-based model for forecasting scene-wide motion modes of multiple traffic agents. Our model transforms local agent-centric embeddings into scene-wide forecasts using a novel latent context module. This module learns a scene-wide latent space from multiple agent-centric embeddings, enabling joint forecasting and interaction modeling. The competitive performance in the Waymo Open Interaction Prediction Challenge demonstrates the effectiveness of our approach. Moreover, we cluster future waypoints in time and space to quantify the interaction between agents. We merge all modes and analyze each mode independently to determine which clusters are resolved through interaction or result in conflict. Our implementation is available at: https://github.com/kit-mrt/future-motion