Panorama-Language Models with a sparse attention module and PanoVQA dataset deliver superior holistic reasoning on 360° adverse omni-scenes compared to stitched pinhole views.
Chameleon: Fast-slow neuro-symbolic lane topology extraction
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
UniUncer is a plug-and-play uncertainty framework that jointly models static and dynamic scene uncertainty inside end-to-end planners, cutting L2 trajectory error 7% on nuScenes and raising EPDMS 10.8% on NavsimV2.
ASSCG is an RWKV-based adaptive gate trained with SFT and GRPO-style RL that makes Query/Cache/Drop decisions for slow LLM guidance in fast-slow autonomous driving planners, improving scores and cutting latency on nuPlan and NAVSIM.
citing papers explorer
-
More than the Sum: Panorama-Language Models for Adverse Omni-Scenes
Panorama-Language Models with a sparse attention module and PanoVQA dataset deliver superior holistic reasoning on 360° adverse omni-scenes compared to stitched pinhole views.
-
UniUncer: Unified Dynamic Static Uncertainty for End to End Driving
UniUncer is a plug-and-play uncertainty framework that jointly models static and dynamic scene uncertainty inside end-to-end planners, cutting L2 trajectory error 7% on nuScenes and raising EPDMS 10.8% on NavsimV2.
-
ASSCG: Just-Right Gating over Chattering for Fast-Slow LLM Planning in Autonomous Driving
ASSCG is an RWKV-based adaptive gate trained with SFT and GRPO-style RL that makes Query/Cache/Drop decisions for slow LLM guidance in fast-slow autonomous driving planners, improving scores and cutting latency on nuPlan and NAVSIM.