Summary of Hard Cases Detection in Motion Prediction by Vision-language Foundation Models, By Yi Yang et al.
Hard Cases Detection in Motion Prediction by Vision-Language Foundation Models
by Yi Yang, Qingwen Zhang, Kei Ikemura, Nazre Batool, John Folkesson
First submitted to arxiv on: 31 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 This paper explores the application of Vision-Language Foundation Models (VLMs) in detecting hard cases for autonomous driving systems. The authors demonstrate that VLMs, such as GPT-4v, can effectively identify challenging scenarios and agents in traffic participant motion prediction on both agent and scenario levels. A pipeline is introduced where VLMs are fed with sequential image frames and designed prompts to detect hard cases, which are then verified by existing prediction models. The authors also show that the detection of hard cases by VLMs improves the training efficiency of the motion prediction pipeline by performing data selection for suggested training samples. Experimental results on NuScenes datasets demonstrate the effectiveness and feasibility of incorporating VLMs with state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make self-driving cars better by using special computers that can understand both images and words. The researchers found that these “vision-language” models, like GPT-4v, are really good at spotting tricky situations on the road, like unusual drivers or bad weather. They created a new way of using these models with other systems to make self-driving cars better. This method helps remove unnecessary data from training and makes the car’s predictions more accurate. The results show that this approach works well with existing methods on real-world data. |
Keywords
» Artificial intelligence » Gpt
