Model Trajectory

Model trajectory research focuses on generating, predicting, and analyzing sequences of states or actions, primarily within the context of autonomous systems, human mobility, and reinforcement learning. Current efforts leverage diverse architectures, including large language models (LLMs), autoregressive models, and neural ordinary differential equations, often incorporating kinematic constraints or behavioral factors to enhance realism and controllability. This work is crucial for improving the safety and efficiency of autonomous vehicles, enabling realistic simulations for various applications (e.g., urban planning, epidemic modeling), and enhancing the interpretability of reinforcement learning algorithms. The development of robust and efficient trajectory models has significant implications across numerous scientific and engineering disciplines.