Trajectory Prediction

Trajectory prediction estimates future paths from past motion and cues such as maps, goals, other agents, and environmental conditions. Common methods include Transformers, graph neural networks, and state-space models, alongside probabilistic approaches such as variational autoencoders, diffusion, and flow matching that represent multiple possible futures. Physics-based models, retrieval, and language or vision-language models are also used, especially when forecasts must support planning or work across domains. A central challenge is balancing accuracy and efficient inference with calibrated uncertainty, realistic and safe behavior, and reliable performance under distribution shifts; displacement benchmarks alone may not capture success in closed-loop settings.