Advanced Techniques in Reinforcement Learning and Deep Learning for Autonomous Vehicle Navigation: Integrating Large Language Models for Real-Time Decision Making

Abstract

The cornerstone of successful autonomous vehicles (AVs) lies in their ability to make safe and adaptable decisions in real-time. This paper investigates a novel approach that integrates reinforcement learning (RL) and deep learning (DL) techniques for AV navigation, further empowered by a large language model (LLM) to bolster real-time decision-making and safety within dynamic environments. We begin by critically analyzing the strengths and limitations of conventional methods, emphasizing the potential of RL for navigating intricate scenarios while acknowledging its data-intensive training requirements. To address these limitations, we propose a novel framework that leverages the power of deep convolutional neural networks (CNNs) for robust environment perception. This framework incorporates an LLM to process and interpret a multitude of data streams, including real-time sensor data, traffic regulations, and historical driving experiences gleaned from past simulations or real-world deployments. This comprehensive data analysis empowers the RL agent to select optimal actions that not only maximize immediate rewards but also prioritize long-term safety and strict adherence to traffic laws. The efficacy of the proposed framework is rigorously evaluated within a high-fidelity simulation environment. The results demonstrate significant improvements in performance metrics compared to baseline approaches, particularly in terms of safety, efficiency, and adherence to traffic regulations.