Summary of Bridge to Real Environment with Hardware-in-the-loop For Wireless Artificial Intelligence Paradigms, by Jeffrey Redondo et al.
Bridge to Real Environment with Hardware-in-the-loop for Wireless Artificial Intelligence Paradigms
by Jeffrey Redondo, Nauman Aslam, Juan Zhang, Zhenhui Yuan
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Networking and Internet Architecture (cs.NI); Signal Processing (eess.SP)
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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 hardware-in-the-loop solution enables testing of machine learning-based solutions for Vehicular Adhoc Networks (VANETs) in both simulated and real-world settings. This approach aims to mitigate the risk of unexpected outcomes when transitioning from simulation-based evaluation to real-world implementation, potentially saving resources. The solution integrates artificial intelligence, multiple services, and HD map data (LiDAR) into a single framework, allowing for comprehensive testing across different environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to test machine learning solutions that will improve communication between cars on the road. Right now, many solutions are tested in a simulated world, which can be cheaper than testing them in real life. However, this approach might not show what happens when these solutions are used in real-life situations. To solve this problem, the authors developed a system that allows testing in both simulated and real-world settings at the same time. |
Keywords
* Artificial intelligence * Machine learning
