Summary of Decentralized Semantic Traffic Control in Avs Using Rl and Dqn For Dynamic Roadblocks, by Emanuel Figetakis et al.
Decentralized Semantic Traffic Control in AVs Using RL and DQN for Dynamic Roadblocks
by Emanuel Figetakis, Yahuza Bello, Ahmed Refaey, Abdallah Shami
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI)
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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 novel DL-based semantic traffic control system empowers Autonomous Vehicles (AVs) to make intelligent lane changes in anticipation of approaching roadblocks. The system leverages Reinforcement Learning (RL) to derive driving decisions, processing sensor data from vehicle dynamics such as speed and location. By entrusting semantic encoding responsibilities to vehicles themselves, the framework reduces latency and improves real-time decision-making. This approach is particularly effective in scenarios where abrupt roadblocks emerge due to factors like road maintenance or accidents, requiring AVs to decide on lane-keeping or lane-changing actions. The system employs a Markov Decision Process (MDP) and Deep Q Learning (DQN) algorithm to uncover viable solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous Vehicles can make smart decisions when they encounter obstacles on the road, like construction zones or accidents. These vehicles use sensors to gather information about their speed, acceleration, and location. But with so much data coming in, it can be hard for them to make quick decisions. To solve this problem, researchers created a new system that lets vehicles process their own information and make smart choices about what to do when they encounter roadblocks. |
Keywords
* Artificial intelligence * Reinforcement learning
