Summary of Towards Explainable, Safe Autonomous Driving with Language Embeddings For Novelty Identification and Active Learning: Framework and Experimental Analysis with Real-world Data Sets, by Ross Greer and Mohan Trivedi
Towards Explainable, Safe Autonomous Driving with Language Embeddings for Novelty Identification and Active Learning: Framework and Experimental Analysis with Real-World Data Sets
by Ross Greer, Mohan Trivedi
First submitted to arxiv on: 11 Feb 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 proposed method integrates language embeddings for active learning in autonomous driving datasets, focusing on novelty detection. The approach employs language-based representations to identify novel scenes, emphasizing the dual purpose of safety takeover responses and active learning. The research presents a clustering experiment using Contrastive Language-Image Pretrained (CLIP) embeddings to organize datasets and detect novelties. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how to use language to help self-driving cars deal with unexpected situations. It uses special language-based representations called CLIP embeddings to group driving data into clusters, identifying new scenes that the car hasn’t seen before. The method also generates text explanations for what makes these new scenes different from others. |
Keywords
* Artificial intelligence * Active learning * Clustering * Novelty detection
