arXiv Machine Learning June 26, 2026 By Qiyuan Wu, Katie Z Luo, Bharath Hariharan, Wei-Lun Chao, Mark Campbell

Rethinking Training & Inference for Forecasting: Linking Winner-Take-All back to GMMs

Read the original on arXiv Machine Learning →

arXiv:2606. 26424v1 Announce Type: new Abstract: Trajectory forecasting for autonomous driving has advanced rapidly, yet representative models often produce uninformative posteriors over forecast modes, causing problems for mode pruning.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Related stories

Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving

Class-Incremental Motion Forecasting

Policy Optimality Measurement for Multi-Vehicle Decision-Making: From Extrinsic Indicators to Intrinsic Quality

Asymmetric Peak-Aware Loss for Peak-Critical Time Series Forecasting

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction

Pivot-Centric Trajectory Prediction: Bridging Long Horizons via Dynamical Guidance