Trajectory prediction plays a crucial role in autonomous driving. Existing mainstream research and continuoual learning-based methods all require training on complete datasets, leading to poor prediction accuracy when sudden changes in scenarios occur and failing to promptly respond and update the model. Whether these methods can make a prediction in real-time and use data instances to update the model immediately(i.e., online learning settings) remains a question. The problem of gradient explosion or vanishing caused by data instance streams also needs to be addressed. Inspired by Hedge Propagation algorithm, we propose Expert Attention Network, a complete online learning framework for trajectory prediction. We introduce expert attention, which adjusts the weights of different depths of network layers, avoiding the model updated slowly due to gradient problem and enabling fast learning of new scenario's knowledge to restore prediction accuracy. Furthermore, we propose a short-term motion trend kernel function which is sensitive to scenario change, allowing the model to respond quickly. To the best of our knowledge, this work is the first attempt to address the online learning problem in trajectory prediction. The experimental results indicate that traditional methods suffer from gradient problems and that our method can quickly reduce prediction errors and reach the state-of-the-art prediction accuracy.

翻译：轨迹预测在自动驾驶中扮演关键角色。现有主流研究及基于持续学习的方法均需在完整数据集上训练，导致场景突变时预测精度下降，且无法及时响应和更新模型。这类方法能否实时进行预测并立即利用数据实例更新模型（即在线学习设置）仍是一大疑问。由数据实例流引发的梯度爆炸或消失问题亦亟待解决。受Hedge Propagation算法启发，我们提出专家注意力网络——一种完整的轨迹预测在线学习框架。通过引入专家注意力机制，调整网络不同深度层的权重，避免因梯度问题导致模型更新缓慢，从而快速学习新场景知识以恢复预测精度。此外，我们提出一种对场景变化敏感的短期运动趋势核函数，使模型能快速响应。据我们所知，本工作是首次尝试解决轨迹预测中的在线学习问题。实验结果表明，传统方法存在梯度问题，而我们的方法能快速降低预测误差，并达到最先进的预测精度。