The spatial-temporal constraints introduced in this work can be combined with reinforcement learning or generative adversarial networks to further enhance prediction accuracy and kinematic feasibility. Reinforcement learning can be used to optimize the trajectory prediction process by rewarding the model for generating kinematically feasible trajectories that adhere to the spatial-temporal constraints. By incorporating reinforcement learning, the model can learn to make better decisions and improve trajectory predictions over time. Generative adversarial networks (GANs) can be utilized to generate more diverse and realistic trajectories by training a generator to produce trajectories that satisfy the spatial-temporal constraints and a discriminator to distinguish between real and generated trajectories. This adversarial training process can help the model generate more realistic and diverse trajectory predictions, enhancing overall performance and robustness.