arXiv Machine Learning June 18, 2026 By Fatemeh Naeinian, Ali Hamza, Haoran Zhu, Anna Choromanska

Zero-Shot Cross-City Generalization in End-to-End Autonomous Driving: Self-Supervised versus Supervised Representations

Read the original on arXiv Machine Learning →

arXiv:2603. 11417v2 Announce Type: replace-cross Abstract: End-to-end autonomous driving models are typically trained on multi-city datasets using supervised ImageNet-pretrained backbones, yet their ability to generalize to unseen cities remains largely unexamined.

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

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Related stories

Shift & Drift: A Zero-Shot Benchmark for Generalizable and Robust Autonomous Driving Motion Planning

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