Summary of Caspformer: Trajectory Prediction From Bev Images with Deformable Attention, by Harsh Yadav et al.
CASPFormer: Trajectory Prediction from BEV Images with Deformable Attention
by Harsh Yadav, Maximilian Schaefer, Kun Zhao, Tobias Meisen
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Context Aware Scene Prediction Transformer (CASPFormer) is a novel motion prediction method for Autonomous Driving (AD) and Advance Driver Assistance Systems (ADAS). Unlike current state-of-the-art methods, CASPFormer can predict multi-modal trajectories from rasterized Bird-Eye-View (BEV) images without requiring High Definition (HD) maps. This makes it more scalable for real-world deployment. The model uses deformable attention to decode vectorized trajectories recurrently, allowing it to focus on important spatial locations in the BEV images. Additionally, learnable mode queries are incorporated to address mode collapse and generate multiple scene-consistent trajectories. CASPFormer is evaluated on the nuScenes dataset, achieving state-of-the-art performance across various metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CASPFormer is a new way for self-driving cars to predict what will happen next in a situation. It uses pictures from a special camera view called Bird-Eye-View (BEV) to make predictions without needing detailed maps. This makes it more practical for real-life use. The system can focus on important parts of the picture and generate multiple possible outcomes. It’s tested on a dataset called nuScenes and performs better than other methods. |
Keywords
» Artificial intelligence » Attention » Multi modal » Transformer
