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20232026
most citedHAIM-DRL: Enhanced Human-in-the-loop Reinforcement Learning for Safe and Efficient Autonomous Driving

83 citations · 134 across the 16 of their papers we have counts for

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Showing 2026Show all

6 papers · 1 filter

cs.CL2026

Large Language Model-Assisted Preparation of Transportation Management Plans: A Case Study with WisDOT WisTMP System

Zihao Sheng, Pei Li, Zilin Huang +6

Work zones are critical yet hazardous components of transportation infrastructure, requiring carefully designed Transportation Management Plans (TMPs) to ensure safety and mobility…

cs.CV2026

Beyond the Leaderboard: Counterfactual Diagnosis of End-to-End and VLA Driving Policies Under Domain Shift

Ruolin Yang, Zilin Huang, Buoyue Wang +4

End-to-end and vision-language-action (VLA) driving policies are compared by leaderboard rank, but a rank reports an outcome, not the behaviour behind it, so it predicts poorly how…

cs.RO2026

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…

cs.CV2026

ExploreVLA: Dense World Modeling and Exploration for End-to-End Autonomous Driving

Zihao Sheng, Xin Ye, Jingru Luo +2

End-to-end autonomous driving models based on Vision-Language-Action (VLA) architectures have shown promising results by learning driving policies through behavior cloning on exper…

cs.RO2026

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…

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…