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Motion Prediction
ForeSight: Multi-View Streaming Joint Object Detection and Trajectory Forecasting
Sandro Papais, Letian Wang, Brian Cheong, Steven L. Waslander
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025.
ForeSight is a novel joint detection and forecasting framework for vision-based 3D perception in autonomous vehicles that seamlessly integrates these tasks using a multi-task streaming and bidirectional learning architecture, allowing for shared query memory and improved temporal consistency.
SmartPretrain: Model-Agnostic and Dataset-Agnostic Representation Learning for Motion Prediction
Yang Zhou, Hao Shao, Letian Wang, Steven L. Waslander, Hongsheng Li, Yu Liu
International Conference on Learning Representations (ICLR), 2025.
SmartPretrain is a general and scalable self-supervised learning framework for motion prediction that can be applied across different models and datasets. By combining contrastive and reconstructive learning strategies, it captures complex spatiotemporal interactions without being restricted by model architecture. SmartPretrain introduces a dataset-agnostic scenario sampling strategy, leveraging diverse datasets to improve robustness and generalization.
SmartRefine: A Scenario-Adaptive Refinement Framework for Efficient Motion Prediction
Yang Zhou, Hao Shao, Letian Wang, Steven L. Waslander, Hongsheng Li, Yu Liu
Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), 2024
We introduce SmartRefine, a novel scenario-adaptive refinement strategy, which can comprehensively adapt refinement configurations based on each scenario's properties. It intelligently selects the number of refinement iterations by introducing a quality score that measures the prediction quality and remaining refinement potential of each scenario.
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