Summary of Trajpred: Trajectory Prediction with Region-based Relation Learning, by Chen Zhou et al.
TrajPRed: Trajectory Prediction with Region-based Relation Learning
by Chen Zhou, Ghassan AlRegib, Armin Parchami, Kunjan Singh
First submitted to arxiv on: 10 Apr 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 This research paper proposes a novel framework for forecasting human trajectories in traffic scenes, crucial for safety in mixed or fully autonomous systems. The framework captures two major stimuli driving human future trajectories: social interactions and stochastic goals. Edge-based relation modeling represents social interactions using pairwise correlations from precise individual states, but this approach can be vulnerable under perturbations. To alleviate these issues, the authors propose a region-based relation learning paradigm that models social interactions via region-wise dynamics of joint states, encoding agent joint information within convolutional feature grids. The framework also incorporates conditional variational autoencoder to realize multi-goal estimation and diverse future prediction, reliably capturing stochastic behavior in test data. The paper evaluates its framework on the ETH-UCY dataset and Stanford Drone Dataset (SDD), outperforming state-of-the-art models by 27.61%/18.20% of ADE/FDE metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper helps us predict where people will go in traffic, which is important for making roads safer with self-driving cars. The study shows that people’s movements are influenced by two main things: what others around them are doing and their own goals. To make good predictions, the researchers use a combination of two techniques: one that looks at how individuals interact with each other and another that takes into account individual goals. This approach is better than previous ones because it can handle situations where people’s movements change unexpectedly. |
Keywords
* Artificial intelligence * Variational autoencoder
