Improving Transferability for Cross-domain Trajectory Prediction via
  Neural Stochastic Differential Equation
Daehee Park Jaewoo Jeong Kuk-Jin Yoon
Multi-agent trajectory prediction is crucial for various practical
applications, spurring the construction of many large-scale trajectory
datasets, including vehicles and pedestrians. However, discrepancies exist
among datasets due to external factors and data acquisition strategies.
External factors include geographical differences and driving styles, while
data acquisition strategies include data acquisition rate, history/prediction
length, and detector/tracker error. Consequently, the proficient performance of
models trained on large-scale datasets has limited transferability on other
small-size datasets, bounding the utilization of existing large-scale datasets.
To address this limitation, we propose a method based on continuous and
stochastic representations of Neural Stochastic Differential Equations (NSDE)
for alleviating discrepancies due to data acquisition strategy. We utilize the
benefits of continuous representation for handling arbitrary time steps and the
use of stochastic representation for handling detector/tracker errors.
Additionally, we propose a dataset-specific diffusion network and its training
framework to handle dataset-specific detection/tracking errors. The
effectiveness of our method is validated against state-of-the-art trajectory
prediction models on the popular benchmark datasets: nuScenes, Argoverse, Lyft,
INTERACTION, and Waymo Open Motion Dataset (WOMD). Improvement in performance
gain on various source and target dataset configurations shows the generalized
competence of our approach in addressing cross-dataset discrepancies.