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.

基于连续和随机表示的神经随机微分方程，提出了一个解决多智能体轨迹预测中数据采集策略差异的方法，并通过 nuScenes、Argoverse、Lyft、INTERACTION 和 Waymo Open Motion Dataset (WOMD) 等基准数据集验证了方法的有效性和广泛适用性。

通过神经随机微分方程提高跨域轨迹预测的可迁移性

Improving Transferability for Cross-domain Trajectory Prediction via  Neural Stochastic Differential Equation

As Machine Learning (ML) systems continue to grow, the demand for relevant
and comprehensive datasets becomes imperative. There is limited study on the
challenges of data acquisition due to ad-hoc processes and lack of consistent
methodologies. We first present an investigation of current data marketplaces,
revealing lack of platforms offering detailed information about datasets,
transparent pricing, standardized data formats. With the objective of inciting
participation from the data-centric AI community, we then introduce the DAM
challenge, a benchmark to model the interaction between the data providers and
acquirers. The benchmark was released as a part of DataPerf. Our evaluation of
the submitted strategies underlines the need for effective data acquisition
strategies in ML.

通过对当前数据市场的调查，揭示了缺乏提供有关数据集的详细信息、透明定价和标准化数据格式的平台，进而提出了基于数据提供者和获取者之间互动建模的 DAM 挑战，验证了在机器学习中有效的数据获取策略的需求。