StandardE2E: A Unified Framework for End-to-End Autonomous Driving Datasets 事件

StandardE2E: A Unified Framework for End-to-End Autonomous Driving Datasets arXiv:2606.04271v1 Announce Type: new Abstract: Autonomous driving has shifted from modular perception-prediction-planning stacks toward end-to-end (E2E) models that map sensor inputs directly to vehicle control, often regularized by auxiliary tasks such as 3D detection, motion forecasting, and HD-map perception. Progress is driven by a fast-growing ecosystem of sensor-rich driving datasets, yet each ships its own file