Summary of Crashformer: a Multimodal Architecture to Predict the Risk Of Crash, by Amin Karimi Monsefi et al.
CrashFormer: A Multimodal Architecture to Predict the Risk of Crash
by Amin Karimi Monsefi, Pouya Shiri, Ahmad Mohammadshirazi, Nastaran Karimi Monsefi, Ron Davies, Sobhan Moosavi, Rajiv Ramnath
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 CrashFormer model is a multi-modal architecture designed to predict the future risk of accidents based on comprehensive input data, including historical accidents, weather information, map images, and demographic information. By utilizing these inputs, CrashFormer aims to provide a more accurate and practical approach to accident prediction, enabling proactive measures to be taken before a crash occurs. The model consists of five components: sequential encoder, image encoder, raw data encoder, feature fusion module, and classifier. Results from real-world experiments in 10 major US cities show that CrashFormer outperforms state-of-the-art models by an average F1-score of 1.8% when using sparse input data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CrashFormer is a new way to predict traffic accidents. Right now, predicting accidents is important for making roads safer and preventing crashes from happening. The problem is that most studies on accident prediction have limitations, like not being able to be used in real life or not working well with different types of data. To fix this, the CrashFormer model uses many different types of input data, such as past accidents, weather information, map images, and demographic information. This helps the model make more accurate predictions about where and when accidents might happen. The results from testing CrashFormer in 10 big US cities show that it does a better job than other models at predicting accidents. |
Keywords
* Artificial intelligence * Encoder * F1 score * Multi modal
