https://mickeykang16.github.io/tags/end-to-end-learning/End-to-End LearningHugo Blox Builder (https://hugoblox.com)en-usTue, 19 May 2026 00:00:00 +0000https://mickeykang16.github.io/media/icon_hu_ff8382b95d9c3dd2.pnghttps://mickeykang16.github.io/tags/end-to-end-learning/https://mickeykang16.github.io/publication/heat/Tue, 19 May 2026 00:00:00 +0000https://mickeykang16.github.io/publication/heat/<p><strong>HEAT</strong> is a trajectory-driven learning framework for end-to-end autonomous driving across heterogeneous domains. A world model couples visual and action latents through ground-truth trajectories to learn domain-agnostic, behavior-aligned representations, which are clustered into trajectory-guided prototypes and episodic memory used to train the E2E-AD model via contrastive learning. The method is evaluated on nuScenes, NAVSIM, and Waymo, achieving state-of-the-art performance in both open-loop planning and closed-loop simulation without domain-specific retraining.</p>