Real-time Fuel Efficiency Prediction and Optimization Using Generative Adversarial Networks

With the rapid development of the Internet of Vehicles (IoV) and deep learning technologies, real-time optimization of fuel efficiency has become an important yet challenging problem. This paper proposes a joint optimization model based on Generative Adversarial Networks (GANs) and Reinforcement Learning (RL) specifically designed to address this issue. Through theoretical analysis, we demonstrate the effectiveness of the model in achieving global optimality. In the experimental phase, we validate the model using the Auto-mpg dataset. The experimental results show that our model reduces the loss value by 25% and improves the AUC value by 15% compared to the baseline model. These numerical results demonstrate a significant advantage of our model in terms of prediction accuracy and optimization capability compared to existing methods.