Forecasting pedestrians' future motions is essential for autonomous driving
systems to safely navigate in urban areas. However, existing prediction
algorithms often overly rely on past observed trajectories and tend to fail
around abrupt dynamic changes, such as when pedestrians suddenly start or stop
walking. We suggest that predicting these highly non-line
该论文提出了一个图变换器结构来改进预测性能，捕捉数据集中不同场所和情景之间的差异，并设计了一种自注意机制和域适应模块来提高模型的泛化能力。此外，还引入了一种考虑跨数据集序列的额外指标用于训练和性能评估目的。使用 ETH 和 UCY 等流行的公共数据集验证和比较了所提出的框架，实验结果表明了我们提出的方案的改进性能。