In autonomous driving, deep learning enabled motion prediction is a popular topic. A critical gap in traditional motion prediction methodologies lies in ensuring equivariance under Euclidean geometric transformations and maintaining invariant interaction relationships. This research introduces a groundbreaking solution by employing EqMotion, a theoretically geometric equivariant and interaction invariant motion prediction model for particles and humans, plus integrating agent-equivariant high-definition (HD) map features for context aware motion prediction in autonomous driving. The use of EqMotion as backbone marks a significant departure from existing methods by rigorously ensuring motion equivariance and interaction invariance. Equivariance here implies that an output motion must be equally transformed under the same Euclidean transformation as an input motion, while interaction invariance preserves the manner in which agents interact despite transformations. These properties make the network robust to arbitrary Euclidean transformations and contribute to more accurate prediction. In addition, we introduce an equivariant method to process the HD map to enrich the spatial understanding of the network while preserving the overall network equivariance property. By applying these technologies, our model is able to achieve high prediction accuracy while maintain a lightweight design and efficient data utilization.

翻译：在自动驾驶领域，基于深度学习的运动预测是热门研究课题。传统运动预测方法的关键缺陷在于无法确保在欧几里得几何变换下的等变性以及保持交互关系的不变性。本研究提出突破性解决方案，采用理论上具有几何等变性和交互不变性的粒子及人类运动预测模型EqMotion作为主干网络，并整合智能体等变的高清地图特征，以实现自动驾驶中的上下文感知运动预测。以EqMotion为骨干网络的方法严格保证了运动等变性和交互不变性，与现有方法形成显著差异。此处等变性指输出运动必须与输入运动在相同欧几里得变换下产生同等变换，而交互不变性则确保智能体间的交互方式在变换中保持不变。这些特性使网络对任意欧几里得变换具有鲁棒性，有助于实现更精准的预测。此外，我们提出等变方法处理高清地图，在保持网络整体等变特性的同时增强空间理解能力。通过应用这些技术，我们的模型能够在保持轻量级设计和高效数据利用率的同时，实现高精度预测。