  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.
