Summary of Simplellm4ad: An End-to-end Vision-language Model with Graph Visual Question Answering For Autonomous Driving, by Peiru Zheng et al.
SimpleLLM4AD: An End-to-End Vision-Language Model with Graph Visual Question Answering for Autonomous Driving
by Peiru Zheng, Yun Zhao, Zhan Gong, Hong Zhu, Shaohua Wu
First submitted to arxiv on: 31 Jul 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 The proposed SimpleLLM4AD method leverages vision-language models (VLMs) to tackle end-to-end autonomous driving (e2eAD). It divides the e2eAD task into four stages: perception, prediction, planning, and behavior. Each stage comprises visual question answering (VQA) pairs that are interconnected in a graph called Graph VQA (GVQA), which is reasoned through VLMs stage-by-stage. Vision transformers (ViT) models process nuScenes visual data, while VLMs interpret and reason about extracted information from visual inputs. The method achieves competitive performance in complex driving scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses large language models to help with autonomous driving. It breaks down the task into four parts: looking at what’s around, predicting where things will go, planning a safe route, and controlling the car. Each part is connected like a puzzle, and the model reasons through it all to make decisions. This approach works well in complex situations. |
Keywords
» Artificial intelligence » Question answering » Vit
