Summary of A Survey For Foundation Models in Autonomous Driving, by Haoxiang Gao and Zhongruo Wang and Yaqian Li and Kaiwen Long and Ming Yang and Yiqing Shen
A Survey for Foundation Models in Autonomous Driving
by Haoxiang Gao, Zhongruo Wang, Yaqian Li, Kaiwen Long, Ming Yang, Yiqing Shen
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
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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 This survey presents a comprehensive review of over 40 research papers that demonstrate the role of foundation models in enhancing autonomous driving (AD). Foundation models have revolutionized natural language processing and computer vision, paving the way for their application in AD. Large language models contribute to planning and simulation in AD through reasoning, code generation, and translation. Vision foundation models are adapted for tasks such as 3D object detection and tracking, creating realistic scenarios for simulation and testing. Multi-modal foundation models integrate diverse inputs, exhibiting exceptional visual understanding and spatial reasoning crucial for end-to-end AD. The survey provides a structured taxonomy categorizing foundation models based on their modalities and functionalities within the AD domain and delves into current research methods. It identifies gaps between existing foundation models and cutting-edge AD approaches, charting future research directions and proposing a roadmap to bridge these gaps. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper reviews how foundation models can help with self-driving cars. Foundation models are like super-smart computers that can learn from lots of data. They’re really good at understanding language and pictures, which is helpful for making decisions about what to do on the road. The review looks at over 40 research papers that show how these foundation models can be used in self-driving cars. It also talks about the different types of foundation models, like ones that understand language and others that are good at recognizing objects. The paper highlights some areas where more work is needed to make self-driving cars even better. |
Keywords
* Artificial intelligence * Multi modal * Natural language processing * Object detection * Tracking * Translation
