Autonomous Transmission Route Planning of Large Sport Utility Vehicle Using Ensemble RNN-Based Reinforcement Learning

The field of autonomous vehicle technology is constantly evolving, and the transmission of large Sport Utility Vehicles (SUVs) presents a unique challenge. In this research, a novel route planning framework specifically designed for large SUVs, leveraging the capabilities of Ensemble Recurrent Neural Networks (RNNs) in conjunction with Reinforcement Learning (RL). The aim is to create an adaptive system capable of learning and optimizing route planning strategies through interactions with the SUV dynamic environment. This research seeks to fill the existing gap in literature by proposing a comprehensive method that caters to the specific requirements of large SUVs, enhancing their efficacy in autonomous transmission scenarios. The proposed method involves training the ensemble RNN using a comprehensive dataset that incorporates diverse driving scenarios for large SUVs. The reinforcement learning component fine-tunes the model based on feedback from simulated and real-world environments.