Summary of Vehicle Behavior Prediction by Episodic-memory Implanted Ndt, By Peining Shen et al.
Vehicle Behavior Prediction by Episodic-Memory Implanted NDT
by Peining Shen, Jianwu Fang, Hongkai Yu, Jianru Xue
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 proposed Episodic Memory implanted Neural Decision Tree (eMem-NDT) model aims to improve the interpretability of behavior prediction in autonomous driving by incorporating hierarchical clustering and neural networks. This approach constructs a tree-like structure that clusters vehicle behavior descriptions, allowing for the alignment of historical features and the inference of future behaviors. The eMem-NDT model is validated on BLVD and LOKI datasets, outperforming other methods while providing clear explainability. By replacing the soft-max layer with eMem-NDT, a pre-trained deep learning model can be fine-tuned for behavior prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In autonomous vehicles, predicting what other cars will do is crucial to stay safe. Right now, computers use special kinds of artificial intelligence (AI) called deep learning to make these predictions. But the problem with this approach is that it’s like a black box – we don’t really know how it makes its decisions. This can be a problem when trying to trust AI in real-life situations. To fix this, researchers created a new model called eMem-NDT (Episodic Memory implanted Neural Decision Tree). It uses a combination of memory and decision-making processes to predict what other cars will do. By doing so, it provides more understandable results than previous methods. |
Keywords
» Artificial intelligence » Alignment » Decision tree » Deep learning » Hierarchical clustering » Inference
