Robust Deep Reinforcement Learning in Autonomous Car Path Planning

The rapid advancements in autonomous vehicle technology have emphasized the need for adaptable and dependable control systems. Deep Reinforcement Learning (DRL) has emerged as a key method to address the complexity involved in autonomous driving. This research explores how DRL can enhance path planning and control mechanisms in self-driving vehicles, with a particular focus on its ability to handle dynamic traffic environments. The challenges in training DRL models, including generalization, safety, and real-time decision-making, are analyzed. The paper also suggests potential research directions for improving DRL's application in autonomous driving, offering insights into its strengths and limitations in the current technological landscape