Summary of Conformal Trajectory Prediction with Multi-view Data Integration in Cooperative Driving, by Xi Chen et al.
Conformal Trajectory Prediction with Multi-View Data Integration in Cooperative Driving
by Xi Chen, Rahul Bhadani, Larry Head
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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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 V2INet framework combines data from various views to predict vehicle trajectories more accurately. By leveraging information from connected technologies like V2V and V2I communication, the model can overcome limitations of single-view sensors. The end-to-end trained framework uses multimodal data and a post-hoc conformal prediction module to generate valid confidence regions. Evaluations on the real-world V2X-Seq dataset show improved performance in terms of FDE and MR using a single GPU. The code is publicly available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research makes vehicle trajectory prediction better by combining information from different views. Usually, this kind of data comes from sensors on cars. But now, with connected technologies like talking to other cars or infrastructure, we can get more useful information. The new V2INet model uses this multi-view data and a special way to check its confidence in predictions. It performs well on real-world data and is easy to use. |
