<p>In this study, we analyze a vulnerable road protection approach utilizing reinforcement learning to extract reward functions, enabling proficient agents to optimize the motion of trajectory objects in real-world scenarios. By leveraging reinforcement learning in simulation, we propose a predictive framework where a reward function guides an agent in decision-making for object movement. Our findings enhance the understanding of grounded simulation learning and control strategies for safeguarding vulnerable road users. Particularly in self-driving cars, service robots, and advanced surveillance systems, accurate trajectory prediction of dynamic agents is critical for planning and safety. This study focuses on predicting the motion trajectory of moving cars, synthesizing and categorizing existing methodologies based on motion modeling strategies and contextual information. Additionally, we summarize key performance metrics and datasets while identifying current safety limitations. Finally, we propose future research directions to improve road safety through advanced trajectory prediction and decision-making models.</p>