Trajectory Prediction
Overview
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.
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