基于可传递GNN的自适应轨迹预测
Adaptive Trajectory Prediction via Transferable GNN
摘要：
行人轨迹预测是自动驾驶和机器人等广泛AI应用的重要组成部分。现有的方法通常假设训练和测试运动遵循相同的模式，而忽略潜在的分布差异（例如，购物中心和街道）。这个问题导致不可避免的性能下降。为了解决这个问题，我们提出了一种新的可转移图神经网络（T-GNN）框架，它在一个统一的框架中联合进行轨迹预测以及域对齐。具体来说，提出了一种域不变的GNN来探索结构运动知识，其中特定于域的知识被减少。此外，一个基于注意力的自适应知识学习模块，进一步提出了探索细粒度的个人层面的知识转移的特征表示。通过这种方式，跨不同轨迹域的差异将得到更好的缓解。更有挑战性的是，设计了实际的弹道预测实验，实验结果验证了我们提出的模型的优越性能。据我们所知，我们的工作是填补不同领域实际行人轨迹预测基准和技术空白的先驱。
英文摘要：
Pedestrian trajectory prediction is an essential component in a wide range of AI applications such as autonomous driving and robotics. Existing methods usually assume the training and testing motions follow the same pattern while ignoring the potential distribution differences (e.g., shopping mall and street). This issue results in inevitable performance decrease. To address this issue, we propose a novel Transferable Graph Neural Network (T-GNN) framework, which jointly conducts trajectory prediction as well as domain alignment in a unified framework. Specifically, a domain-invariant GNN is proposed to explore the structural motion knowledge where the domain-specific knowledge is reduced. Moreover, an attention-based adaptive knowledge learning module is further proposed to explore fine-grained individual-level feature representations for knowledge transfer. By this way, disparities across different trajectory domains will be better alleviated. More challenging while practical trajectory prediction experiments are designed, and the experimental results verify the superior performance of our proposed model. To the best of our knowledge, our work is the pioneer which fills the gap in benchmarks and techniques for practical pedestrian trajectory prediction across different domains.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
