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.

基于 Hedge Propagation 算法的启发，我们提出了 Expert Attention Network，它是一种完整的在线学习框架，用于轨迹预测。我们引入了专家注意力机制，通过调整不同深度网络层的权重，避免了由于梯度问题导致模型更新缓慢，使其能够快速学习新场景的知识以恢复预测精度。此外，我们提出了一种对场景变化敏感的短期运动趋势核函数，使得模型能够快速响应。实验结果表明，传统方法存在梯度问题，而我们的方法可以快速减小预测误差并达到最先进的预测准确性。

EANet: 在线轨迹预测的专家注意力网络

EANet: Expert Attention Network for Online Trajectory Prediction

We introduce the HSIC (Hilbert-Schmidt independence criterion) bottleneck for
training deep neural networks. The HSIC bottleneck is an alternative to the
conventional cross-entropy loss and backpropagation that has a number of
distinct advantages. It mitigates exploding and vanishing gradients, resulting
in the ability to learn very deep networks without skip connections. There is
no requirement for symmetric feedback or update locking. We find that the HSIC
bottleneck provides performance on MNIST/FashionMNIST/CIFAR10 classification
comparable to backpropagation with a cross-entropy target, even when the system
is not encouraged to make the output resemble the classification labels.
Appending a single layer trained with SGD (without backpropagation) to reformat
the information further improves performance.

介绍了使用 HSIC 瓶颈训练深度神经网络，它可以缓解爆炸和消失梯度的问题，实现了在没有跳跃连接的情况下学习非常深的网络，而且在不需要对称反馈或更新锁定的情况下，可以获得 MNIST / FashionMNIST / CIFAR10 分类等优异的性能。同时，使用 SGD 训练单独的一层可以进一步提高性能。