In the modern world, the development of Artificial Intelligence (AI) has contributed to improvements in various areas, including automation, computer vision, fraud detection, and more. AI can be leveraged to enhance the efficiency of Autonomous Smart Traffic Management (ASTM) systems and reduce traffic congestion rates. This paper presents an Autonomous Smart Traffic Management (STM) system that uses AI to improve traffic flow rates. The system employs the YOLO V5 Convolutional Neural Network to detect vehicles in traffic management images. Additionally, it predicts the number of vehicles for the next 12 hours 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 vehicle predictions, aided by AI. From the results of the RNN-LSTM model for predicting vehicle numbers over the next 12 hours, we observe that the model predicts traffic with a Mean Squared Error (MSE) of 4.521 vehicles and a Root Mean Squared Error (RMSE) of 2.232 vehicles. After simulating the STM system in the CARLA simulation environment, we found that the Traffic Management Congestion Flow Rate with ASTM (21 vehicles per minute) is 50\% higher than the rate without STM (around 15 vehicles per minute). Additionally, the Traffic Management Vehicle Pass Delay with STM (5 seconds per vehicle) is 70\% lower than without STM (around 12 seconds per vehicle). These results demonstrate that the STM system using AI can increase traffic flow by 50\% and reduce vehicle pass delays by 70\%.

翻译：在现代社会中，人工智能（AI）的发展推动了自动化、计算机视觉、欺诈检测等多个领域的进步。利用人工智能可以提升自主智能交通管理（ASTM）系统的效率并降低交通拥堵率。本文提出了一种采用人工智能改善交通流量的自主智能交通管理（STM）系统。该系统利用YOLO V5卷积神经网络检测交通管理图像中的车辆。此外，通过结合长短期记忆的循环神经网络（RNN-LSTM）预测未来12小时的车辆数量。基于这些车辆预测结果，智能交通管理周期时长分析在人工智能辅助下调控交通信号周期。从RNN-LSTM模型对未来12小时车辆数的预测结果可见，该模型预测交通流量的均方误差（MSE）为4.521辆车，均方根误差（RMSE）为2.232辆车。在CARLA仿真环境中对STM系统进行模拟后，我们发现采用ASTM的交通管理拥堵流率（每分钟21辆车）比未使用STM时（约每分钟15辆车）提高了50%。同时，采用STM的交通管理车辆通过延迟（每辆车5秒）较未使用STM时（约每辆车12秒）降低了70%。这些结果表明，采用人工智能的STM系统能够将交通流量提升50%，并将车辆通过延迟减少70%。