Summary of Goalnet: Goal Areas Oriented Pedestrian Trajectory Prediction, by Ching-lin Lee et al.
GoalNet: Goal Areas Oriented Pedestrian Trajectory Prediction
by Ching-Lin Lee, Zhi-Xuan Wang, Kuan-Ting Lai, Amar Fadillah
First submitted to arxiv on: 29 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 A novel approach to pedestrian trajectory prediction in autonomous driving is presented, addressing the limitations of existing methods that solely rely on past trajectories. The proposed GoalNet network leverages scene context and observed trajectory information to predict goal points, which are then used to predict future trajectories. This framework improves uncertainty by limiting it to a few target areas, representing pedestrian goals. The model efficiently predicts both trajectories and bounding boxes, outperforming the state-of-the-art by 48.7% on the JAAD dataset and 40.8% on the PIE dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Predicting where pedestrians will go is important for self-driving cars to stay safe. Most current methods try to guess many possible paths a pedestrian might take based on what they’ve done before, but they don’t consider the surrounding environment or what the pedestrian is trying to achieve. This paper proposes a new approach that first predicts where the pedestrian is heading and then uses that information to predict their actual path. By using both the current situation and past behavior, this method is much better at predicting where pedestrians will go. It can even tell you exactly where they are in space. |
