Summary of Review Of the Learning-based Camera and Lidar Simulation Methods For Autonomous Driving Systems, by Hamed Haghighi et al.
Review of the Learning-based Camera and Lidar Simulation Methods for Autonomous Driving Systems
by Hamed Haghighi, Xiaomeng Wang, Hao Jing, Mehrdad Dianati
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Graphics (cs.GR); Machine Learning (cs.LG); 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 paper reviews current state-of-the-art learning-based methods for simulating camera and Lidar sensors in Autonomous Driving Systems (ADS). With deep learning-based perception models on the rise, developing realistic simulation methods is crucial for testing ADS. Traditional sensor simulation methods rely on computationally expensive physics-based algorithms, whereas learning-based models show promise. The paper covers two types of learning-based approaches: raw-data-based and object-based models. Raw-data-based methods are discussed in terms of employed learning strategies, while object-based models are categorized based on error type considered. Additionally, the paper highlights commonly used validation techniques for evaluating perception sensor models and identifies existing research gaps. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make fake camera and Lidar data that’s realistic enough to test self-driving cars. Right now, making this kind of data is hard because it needs complex physics calculations. But a new way to do this using learning-based models is getting attention. The researchers look at two ways to do this: one based on raw data and the other based on objects. They also talk about how to check if these fake datasets are good enough for testing. |
Keywords
* Artificial intelligence * Attention * Deep learning
