Summary of Multi-agent Assessment with Qos Enhancement For Hd Map Updates in a Vehicular Network, by Jeffrey Redondo et al.
Multi-agent Assessment with QoS Enhancement for HD Map Updates in a Vehicular Network
by Jeffrey Redondo, Nauman Aslam, Juan Zhang, Zhenhui Yuan
First submitted to arxiv on: 31 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 abstract proposes a Q-learning single-agent solution for improving network performance in vehicular ad hoc networks (VANET) by reducing computational load and enhancing scalability. The authors utilize a multi-agent approach to take advantage of a smaller state and action space, resulting in improved time latencies in various test cases including voice, video, HD Maps, and best-effort scenarios. Compared to the single-agent approach, the proposed solution achieves significant improvements in performance metrics such as 40.4%, 36%, 43%, and 12% respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way for autonomous vehicles (AVs) to share information more efficiently, reducing the need for powerful computers. The idea is to use an algorithm called Q-learning to make better decisions about how to communicate with other nearby vehicles. This approach has several advantages, including lower computational costs and improved compatibility between different technologies. To test this solution, the authors ran simulations involving different scenarios, such as voice calls, video streaming, and mapping updates. The results show that this new method can significantly reduce latency times compared to current methods. |
