Summary of Pip-net: Pedestrian Intention Prediction in the Wild, by Mohsen Azarmi et al.
PIP-Net: Pedestrian Intention Prediction in the Wild
by Mohsen Azarmi, Mahdi Rezaei, He Wang, Sebastien Glaser
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE); Image and Video Processing (eess.IV); Machine Learning (stat.ML)
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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 presents PIP-Net, a novel framework for predicting pedestrian crossing intentions by Autonomous Vehicles (AVs) in urban scenarios. The model uses kinematic data and spatial features to predict intentions up to 4 seconds in advance, outperforming state-of-the-art performance. Two variants of PIP-Net are designed for different camera mounts and setups. Additionally, the paper introduces a categorical depth feature map and local motion flow feature to enhance visual representation of road users and scene dynamics. The impact of expanding the camera’s field of view is also explored. Overall, this work advances pedestrian intention prediction in automated driving scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine if cars could predict when people are about to cross the street! This paper shows how to make that happen using special cameras on self-driving cars. The researchers created a new system called PIP-Net that can predict when someone is going to cross the road up to 4 seconds in advance. They tested it in real-life situations and found that it worked really well. They also made a special dataset with lots of examples for training the system, which will help make self-driving cars safer. |
Keywords
» Artificial intelligence » Feature map
