10 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…
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…
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…
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…