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cs.CV2026
FPD-Drive: Future Policy Distillation for End-to-End Autonomous Driving
Li-Heng Chen, Qifeng Chen, Chengwei Wu +4
End-to-end autonomous driving models are typically trained on real-world driving logs, which record only the trajectory actually executed in each scene. This provides limited super…
cs.CV2026
MM-Future: Multi-Mode Joint World-Action Modeling for Autonomous Driving
Shuai Liu, Hechangle Gong, Hao Jiang +5
Autonomous driving involves coupled decision-making and scene evolution under multi-mode uncertainty. To capture this coupling and uncertainty, we introduce MM-Future, a world-acti…
cs.CV2023★ 5 cited
Open-sourced Data Ecosystem in Autonomous Driving: the Present and Future
Hongyang Li, Yang Li, Huijie Wang +16
With the continuous maturation and application of autonomous driving technology, a systematic examination of open-source autonomous driving datasets becomes instrumental in fosteri…