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EqDrive: Efficient Equivariant Motion Forecasting with Multi-Modality for Autonomous Driving
Forecasting vehicular motions in autonomous driving requires a deep understanding of agent interactions and the preservation of motion equivariance under Euclidean geometric transformations.
Equivariant Map and Agent Geometry for Autonomous Driving Motion Prediction
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.
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