Optimizing Autonomous Vehicle Navigation using Deep Reinforcement Learning and Generative AI Models: An Empirical Study

Research on navigation for autonomous vehicles (AVs) is crucial to improving the safety, efficiency, and adaptability of self-driving systems. The integration of Deep Reinforcement Learning (DRL) and Generative AI models is being explored in this paper aims to improve AV navigation in dynamic environments. Utilizing DRL, the paper also investigates how AVs can independently discover optimal driving strategies by interacting with simulated environments, also employing Generative AI models to reproduce and produce more valuable arbitrary navigation along uncharted or changing an environment. Oct word make mentioned perform path planning obstacle avoidance real time decision-making empirical study In summary, the results show the effectiveness of predicting future trajectories and reducing the road uncertainties in order to achieve superior performance without compromising driving safety; a hybrid approach that could feasibly translate well into application in production-grade self-driving systems.