论文标题
使用均值连续卷积的轨迹预测
Trajectory Prediction using Equivariant Continuous Convolution
论文作者
论文摘要
轨迹预测是许多AI应用的关键部分，例如自动驾驶汽车的安全操作。但是，当前的方法容易做出不一致和身体上不切实际的预测。我们利用流体动力学的见解来克服这一限制，通过考虑现实世界中的内部对称性。我们提出了一个新型模型，即均衡持续卷积（ECCO），以改善轨迹预测。 ECCO使用旋转等值的连续卷积来嵌入系统的对称性。在车辆和行人轨迹数据集上，ECCO的参数明显较少。它也是更有效的样本，从任何方向中的几个数据点自动概括。最后，ECCO通过均衡性提高了概括，从而产生了更加一致的预测。我们的方法为提高深度学习模型的信任和透明度提供了新的视角。
Trajectory prediction is a critical part of many AI applications, for example, the safe operation of autonomous vehicles. However, current methods are prone to making inconsistent and physically unrealistic predictions. We leverage insights from fluid dynamics to overcome this limitation by considering internal symmetry in real-world trajectories. We propose a novel model, Equivariant Continous COnvolution (ECCO) for improved trajectory prediction. ECCO uses rotationally-equivariant continuous convolutions to embed the symmetries of the system. On both vehicle and pedestrian trajectory datasets, ECCO attains competitive accuracy with significantly fewer parameters. It is also more sample efficient, generalizing automatically from few data points in any orientation. Lastly, ECCO improves generalization with equivariance, resulting in more physically consistent predictions. Our method provides a fresh perspective towards increasing trust and transparency in deep learning models.
