2 papers
cs.RO2026
EvoAD: Self-Evolving Autonomous Driving via Latent World-Model Guided Post-hoc Reflection
Junjie Yang, Qiming Zhang, Yuyan Li +12
End-to-end autonomous driving systems have achieved rapid progress by learning unified policies that map sensor observations directly to driving trajectories. However, once deploye…
cs.AI2026
PLAN-S: Bridging Planning with Latent Style Dynamics for Autonomous Driving World Models
Xiaoyun Qiu, Jingtao He, Yijie Chen +4
Latent world models (LWMs) have strengthened end-to-end autonomous driving by forecasting compact scene dynamics for downstream planning. However, existing LWM-based planners usual…