Summary of Synthetic Data Generation Framework, Dataset, and Efficient Deep Model For Pedestrian Intention Prediction, by Muhammad Naveed Riaz et al.
Synthetic Data Generation Framework, Dataset, and Efficient Deep Model for Pedestrian Intention Prediction
by Muhammad Naveed Riaz, Maciej Wielgosz, Abel Garcia Romera, Antonio M. Lopez
First submitted to arxiv on: 12 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 paper introduces ARCANE, a framework for generating synthetic datasets of pedestrian crossing and non-crossing (C/NC) scenarios from sequential images. This addresses the scarcity of such datasets, which is crucial for developing accurate models for autonomous driving. The authors demonstrate the effectiveness of ARCANE by creating PedSynth, a large and diverse dataset that complements existing real-world datasets like JAAD and PIE. Additionally, they propose PedGNN, a deep model with low memory footprint and fast processing speed, suitable for onboard deployment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps create safer autonomous vehicles by predicting when pedestrians will cross the road. It’s hard to make accurate models because there aren’t many examples of different situations where pedestrians do or don’t cross. The authors invented a way to create more scenarios using computer programs. This helps make better models for predicting when pedestrians will cross. They also made a special model that can run quickly and use little memory, making it suitable for being used in self-driving cars. |
