2 citations · 7 across the 8 of their papers we have counts for
15 papers
Sim2Real-AD: A Modular Sim-to-Real Framework for Deploying VLM-Guided Reinforcement Learning in Real-World Autonomous Driving
Zilin Huang, Zhengyang Wan, Zihao Sheng +3
Vision-language-model (VLM)-guided reinforcement learning (RL) has recently attracted significant attention for it, replacing brittle hand-crafted rewards with semantically grounde…
DriveVLM-RL: Neuroscience-Inspired Reinforcement Learning with Vision-Language Models for Safe and Deployable Autonomous Driving
Zilin Huang, Zihao Sheng, Zhengyang Wan +4
Traditional reinforcement learning (RL) methods rely on manually engineered rewards or sparse collision signals, which fail to capture the rich contextual understanding required fo…
Found-RL: foundation model-enhanced reinforcement learning for autonomous driving
Yansong Qu, Zihao Sheng, Zilin Huang +6
Reinforcement Learning (RL) has emerged as a dominant paradigm for end-to-end autonomous driving (AD). However, RL suffers from sample inefficiency and a lack of semantic interpret…
SafePLUG: Empowering Multimodal LLMs with Pixel-Level Insight and Temporal Grounding for Traffic Accident Understanding
Zihao Sheng, Zilin Huang, Yansong Qu +5
Multimodal large language models (MLLMs) have achieved remarkable progress across a range of vision-language tasks and demonstrate strong potential for traffic accident understandi…
A Survey on Vision-Language-Action Models for Autonomous Driving
Sicong Jiang, Zilin Huang, Kangan Qian +17
The rapid progress of multimodal large language models (MLLM) has paved the way for Vision-Language-Action (VLA) paradigms, which integrate visual perception, natural language unde…
Sky-Drive: A Distributed Multi-Agent Simulation Platform for Human-AI Collaborative and Socially-Aware Future Transportation
Zilin Huang, Zihao Sheng, Zhengyang Wan +10
Recent advances in autonomous system simulation platforms have significantly enhanced the safe and scalable testing of driving policies. However, existing simulators do not yet ful…