activity
20242026
most citedVLM-SAFE: Vision-Language Model-Guided Safety-Aware Reinforcement Learning with World Models for Autonomous Driving

1 citations · 1 across the 1 of their papers we have counts for

collaborators

7 papers

cs.RO20261 cited

VLM-SAFE: Vision-Language Model-Guided Safety-Aware Reinforcement Learning with World Models for Autonomous Driving

Yansong Qu, Zilin Huang, Zihao Sheng +4

Autonomous driving policy learning with reinforcement learning (RL) is fundamentally limited by low sample efficiency, weak generalization, and a dependence on unsafe online trial-…

cs.AI2026

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…

cs.CV2025

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…

cs.RO2025

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…

cs.RO2025

CurricuVLM: Towards Safe Autonomous Driving via Personalized Safety-Critical Curriculum Learning with Vision-Language Models

Zihao Sheng, Zilin Huang, Yansong Qu +3

Ensuring safety in autonomous driving systems remains a critical challenge, particularly in handling rare but potentially catastrophic safety-critical scenarios. While existing res…

cs.CV2025

MetaSSC: Enhancing 3D Semantic Scene Completion for Autonomous Driving through Meta-Learning and Long-sequence Modeling

Yansong Qu, Zixuan Xu, Zilin Huang +3

Semantic scene completion (SSC) is essential for achieving comprehensive perception in autonomous driving systems. However, existing SSC methods often overlook the high deployment…