Summary of Syntrac: a Synthetic Dataset For Traffic Signal Control From Traffic Monitoring Cameras, by Tiejin Chen et al.
SynTraC: A Synthetic Dataset for Traffic Signal Control from Traffic Monitoring Cameras
by Tiejin Chen, Prithvi Shirke, Bharatesh Chakravarthi, Arpitsinh Vaghela, Longchao Da, Duo Lu, Yezhou Yang, Hua Wei
First submitted to arxiv on: 18 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 SynTraC dataset bridges the gap between simulated environments and real-world traffic management challenges by providing an image-based traffic signal control dataset. Unlike traditional datasets, SynTraC provides real-style images from the CARLA simulator with annotated features and traffic signal states, along with diverse real-world scenarios and varying weather and times of day. The dataset also includes different reward values for advanced traffic signal control algorithms like reinforcement learning. Experiments demonstrate that image-based methods struggle compared to feature-based methods, indicating the potential for SynTraC to guide future algorithm development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SynTraC is a new way to help computers manage traffic lights better. Right now, we use fake data from simulators to train computers to control traffic signals. But this data isn’t very realistic and doesn’t account for real-world challenges like weather and time of day. SynTraC changes that by providing real images of traffic scenes with annotated features and different scenarios. This will help developers create more advanced algorithms that can actually work in the real world. |
Keywords
* Artificial intelligence * Reinforcement learning
