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Found 5 papers, 2 papers with code
CILF:Causality Inspired Learning Framework for Out-of-Distribution Vehicle Trajectory Prediction
To leverage causal features for prediction, we propose a Causal Inspired Learning Framework (CILF), which includes three steps: 1) extracting domain-invariant causal feature by means of an invariance loss, 2) extracting domain variant feature by domain contrastive learning, and 3) separating domain-variant causal and non-causal feature by encouraging causal sufficiency.
Contrastive Embedding Distribution Refinement and Entropy-Aware Attention for 3D Point Cloud Classification
Learning a powerful representation from point clouds is a fundamental and challenging problem in the field of computer vision.
Hierarchical Motion Encoder-Decoder Network for Trajectory Forecasting
HMNet first infers the hierarchical difference on motions to encode physically compliant patterns with high expressivity of moving trends and driving intentions.
Recursive Least Squares Based Refinement Network for the Rollout Trajectory Prediction Methods
Trajectory prediction plays a pivotal role in the field of intelligent vehicles.
Spatial-Channel Transformer Network for Trajectory Prediction on the Traffic Scenes
In this paper, we present a Spatial-Channel Transformer Network for trajectory prediction with attention functions.
