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cs.RO2026
PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving
Kang Ding, Zhigui Lin, Hongsong Wang +5
This letter presents PrismAD, a decoupled end-to-end autonomous driving framework based on a Semantic Mixture-of-Planners. Existing planners usually aggregate heterogeneous scene t…
cs.RO2026
AnchorVLA: Bridging Discrete Decisions and Continuous Trajectories for Vision-Language-Action Planning
Qi Liu, Yabei Li, Hongsong Wang +2
Autonomous driving planning requires translating navigation intent, traffic rules, dynamic interactions, and language instructions into executable continuous trajectories. Vision-L…
cs.CV2026
Hierarchically Decoupled Mixture-of-Experts for Robust Traffic Sign Recognition in Complex Driving Scenarios
Mingxiao Wang, Xiaozhen Qu, Bolin Gao +2
Traffic sign detection is a fundamental component of environmental perception in autonomous driving and intelligent transportation systems. However, most existing detectors rely on…