Summary of Esp: Extro-spective Prediction For Long-term Behavior Reasoning in Emergency Scenarios, by Dingrui Wang et al.
ESP: Extro-Spective Prediction for Long-term Behavior Reasoning in Emergency Scenarios
by Dingrui Wang, Zheyuan Lai, Yuda Li, Yi Wu, Yuexin Ma, Johannes Betz, Ruigang Yang, Wei Li
First submitted to arxiv on: 7 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 addresses the crucial task of predicting emergency scenarios in autonomous driving, which requires reliable and timely predictions. The authors create a new dataset, ESP, designed to capture long-tailed and varied historical states leading up to emergencies. A flexible feature encoder is introduced for various prediction methods, showcasing consistent performance improvements. Additionally, a novel metric, CTE, is proposed to evaluate prediction performance in time-sensitive emergency events. The paper also explores the potential application of integrating ESP features with ChatGPT. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper focuses on predicting emergency scenarios in autonomous driving. It creates a new dataset called ESP that helps predict emergencies by looking at how things were before an emergency happened. The authors show that their method works well and is better than other methods for this task. They also introduce a new way to measure how good the predictions are, which is important because time matters in emergency situations. |
Keywords
» Artificial intelligence » Encoder
