Summary of Astm :autonomous Smart Traffic Management System Using Artificial Intelligence Cnn and Lstm, by Christofel Rio Goenawan
ASTM :Autonomous Smart Traffic Management System Using Artificial Intelligence CNN and LSTM
by Christofel Rio Goenawan
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 Autonomous Smart Traffic Management (STM) system leverages Artificial Intelligence (AI) to enhance the efficiency of ASTM systems, reducing traffic congestion rates. The system employs YOLO V5 Convolutional Neural Network for detecting vehicles in traffic management images and predicts vehicle numbers using a Recurrent Neural Network with Long Short-Term Memory (RNN-LSTM). The Smart Traffic Management Cycle Length Analysis manages the traffic cycle length based on these predictions, aided by AI. The RNN-LSTM model achieves a Mean Squared Error (MSE) of 4.521 vehicles and Root Mean Squared Error (RMSE) of 2.232 vehicles in predicting vehicle numbers over the next 12 hours. Simulation results in CARLA demonstrate that the STM system increases traffic flow by 50% and reduces vehicle pass delays by 70%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new system to help manage traffic, using artificial intelligence (AI). The system uses two AI models: one to detect vehicles on roads and another to predict how many vehicles will be on the road in the next 12 hours. This information helps decide when to let traffic flow freely or stop it for safety reasons. The results show that this system can increase traffic flow by 50% and reduce delays by 70%. |
Keywords
» Artificial intelligence » Lstm » Mse » Neural network » Rnn » Yolo
