Summary of Flow-guided Motion Prediction with Semantics and Dynamic Occupancy Grid Maps, by Rabbia Asghar et al.
Flow-guided Motion Prediction with Semantics and Dynamic Occupancy Grid Maps
by Rabbia Asghar, Wenqian Liu, Lukas Rummelhard, Anne Spalanzani, Christian Laugier
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel multi-task framework that combines Occupancy Grid Maps (OGMs) with deep learning methods to predict both future vehicle semantic grids and the flow of the scene. The framework leverages dynamic OGMs and semantic information, allowing for the generation of warped semantic grids and improved prediction capabilities on real-world datasets like NuScenes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper’s main goal is to accurately predict driving scenes, which is crucial for road safety and autonomous driving. It uses a combination of Occupancy Grid Maps (OGMs) and deep learning methods to predict the evolution of scenes and learn complex behaviors. The framework also predicts flow or velocity vectors in the scene, which helps generate warped semantic grids. |
Keywords
» Artificial intelligence » Deep learning » Multi task
