Summary of Ofmpnet: Deep End-to-end Model For Occupancy and Flow Prediction in Urban Environment, by Youshaa Murhij and Dmitry Yudin
OFMPNet: Deep End-to-End Model for Occupancy and Flow Prediction in Urban Environment
by Youshaa Murhij, Dmitry Yudin
First submitted to arxiv on: 2 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 A novel end-to-end neural network methodology is proposed for predicting the future behaviors of all dynamic objects in the environment, leveraging occupancy maps and motion flow. The OFMPNet model incorporates various encoder-decoder architectures, including transformer, attention-based, or convolutional units, and decoder components such as convolutional modules and recurrent blocks. A time-weighted motion flow loss is also introduced, which significantly improves performance on the Waymo Occupancy and Flow Prediction benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to predict where objects in a scene will move next. It uses special maps that show what’s happening in the environment and how things are moving around. The approach combines different ideas from machine learning to create a powerful model called OFMPNet. This model takes in information like road images, maps of the scene, and past movements to make predictions about future movements. The results are impressive, with the best performance yet on a challenging benchmark. |
Keywords
» Artificial intelligence » Attention » Decoder » Encoder decoder » Machine learning » Neural network » Transformer
