تقرير
SceneMotion: From Agent-Centric Embeddings to Scene-Wide Forecasts
| العنوان: | SceneMotion: From Agent-Centric Embeddings to Scene-Wide Forecasts |
|---|---|
| المؤلفون: | Wagner, Royden, Tas, Ömer Sahin, Steiner, Marlon, Konstantinidis, Fabian, Königshof, Hendrik, Klemp, Marvin, Fernandez, Carlos, Stiller, Christoph |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics |
| الوصف: | 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 Comment: 7 pages, 3 figures, ITSC 2024; v2: added details about waypoint clustering |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2408.01537 |
| رقم الأكسشن: | edsarx.2408.01537 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |