75 citations · 76 across the 10 of their papers we have counts for
11 papers
Autonomous discovery of traffic laws with AI traffic scientists
Xingyuan Dai, Yue Liu, Xiaoyan Gong +9
Universal traffic laws describe recurrent patterns in congestion, mobility and driving behavior across cities, providing a scientific basis for transportation planning, management…
CogRail: Benchmarking VLMs in Cognitive Intrusion Perception for Intelligent Railway Transportation Systems
Yonglin Tian, Qiyao Zhang, Wei Xu +9
Accurate and early perception of potential intrusion targets is essential for ensuring the safety of railway transportation systems. However, most existing systems focus narrowly o…
SUMO-MCP: Leveraging the Model Context Protocol for Autonomous Traffic Simulation and Optimization
Chenglong Ye, Gang Xiong, Junyou Shang +3
Traffic simulation tools, such as SUMO, are essential for urban mobility research. However, such tools remain challenging for users due to complex manual workflows involving networ…
Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework
Mengmeng Zhang, Xingyuan Dai, Yicheng Sun +6
Although the Segment Anything Model (SAM) is highly effective in natural image segmentation, it requires dependencies on prompts, which limits its applicability to medical imaging…
Context-Aware Probabilistic Modeling with LLM for Multimodal Time Series Forecasting
Yueyang Yao, Jiajun Li, Xingyuan Dai +4
Time series forecasting is important for applications spanning energy markets, climate analysis, and traffic management. However, existing methods struggle to effectively integrate…
Offline Reinforcement Learning with Discrete Diffusion Skills
RuiXi Qiao, Jie Cheng, Xingyuan Dai +2
Skills have been introduced to offline reinforcement learning (RL) as temporal abstractions to tackle complex, long-horizon tasks, promoting consistent behavior and enabling meanin…
Evaluation of Safety Cognition Capability in Vision-Language Models for Autonomous Driving
Enming Zhang, Peizhe Gong, Xingyuan Dai +3
Ensuring the safety of vision-language models (VLMs) in autonomous driving systems is of paramount importance, yet existing research has largely focused on conventional benchmarks…
UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobility
Yonglin Tian, Fei Lin, Yiduo Li +11
Low-altitude mobility, exemplified by unmanned aerial vehicles (UAVs), has introduced transformative advancements across various domains, like transportation, logistics, and agricu…
MiniDrive: More Efficient Vision-Language Models with Multi-Level 2D Features as Text Tokens for Autonomous Driving
Enming Zhang, Xingyuan Dai, Min Huang +2
Vision-language models (VLMs) serve as general-purpose end-to-end models in autonomous driving, performing subtasks such as prediction, planning, and perception through question-an…
SC-Tune: Unleashing Self-Consistent Referential Comprehension in Large Vision Language Models
Tongtian Yue, Jie Cheng, Longteng Guo +6
Recent trends in Large Vision Language Models (LVLMs) research have been increasingly focusing on advancing beyond general image understanding towards more nuanced, object-level re…
RIME: Robust Preference-based Reinforcement Learning with Noisy Preferences
Jie Cheng, Gang Xiong, Xingyuan Dai +3
Preference-based Reinforcement Learning (PbRL) circumvents the need for reward engineering by harnessing human preferences as the reward signal. However, current PbRL methods exces…