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In autonomous driving, deep learning enabled motion prediction is a populartopic. A critical gap in traditional motion prediction methodologies lies inensuring equivariance under Euclidean geometric transformations and maintaininginvariant interaction relationships. This research introduces a groundbreakingsolution by employing EqMotion, a theoretically geometric equivariant andinteraction invariant motion prediction model for particles and humans, plusintegrating agent-equivariant high-definition (HD) map features for contextaware motion prediction in autonomous driving. The use of EqMotion as backbonemarks a significant departure from existing methods by rigorously ensuringmotion equivariance and interaction invariance. Equivariance here implies thatan output motion must be equally transformed under the same Euclideantransformation as an input motion, while interaction invariance preserves themanner in which agents interact despite transformations. These properties makethe network robust to arbitrary Euclidean transformations and contribute tomore accurate prediction. In addition, we introduce an equivariant method toprocess the HD map to enrich the spatial understanding of the network whilepreserving the overall network equivariance property. By applying thesetechnologies, our model is able to achieve high prediction accuracy whilemaintain a lightweight design and efficient data utilization.

Equivariant Map and Agent Geometry for Autonomous Driving Motion Prediction