6 papers
Automating Crash Diagram Generation Using Vision-Language Models: A Case Study on Multi-Lane Roundabouts
Xiao Lu, Hao Zhen, Jidong J. Yang
Crash diagrams are essential tools in transportation safety analysis, yet their manual preparation remains time-consuming and prone to human variability. This study investigates th…
Multi-view Phase-aware Pedestrian-Vehicle Incident Reasoning Framework with Vision-Language Models
Hao Zhen, Yunxiang Yang, Jidong J. Yang
Pedestrian-vehicle incidents remain a critical urban safety challenge, with pedestrians accounting for over 20% of global traffic fatalities. Although existing video-based systems…
CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis
Hao Zhen, Jidong J. Yang
Road crashes claim over 1.3 million lives annually worldwide and incur global economic losses exceeding $1.8 trillion. Such profound societal and financial impacts underscore the…
Feature Group Tabular Transformer: A Novel Approach to Traffic Crash Modeling and Causality Analysis
Oscar Lares, Hao Zhen, Jidong J. Yang
Reliable and interpretable traffic crash modeling is essential for understanding causality and improving road safety. This study introduces a novel approach to predicting collision…
Enhancing Nighttime Vehicle Detection with Day-to-Night Style Transfer and Labeling-Free Augmentation
Yunxiang Yang, Hao Zhen, Yongcan Huang +1
Existing deep learning-based object detection models perform well under daytime conditions but face significant challenges at night, primarily because they are predominantly traine…
Leveraging Large Language Models with Chain-of-Thought and Prompt Engineering for Traffic Crash Severity Analysis and Inference
Hao Zhen, Yucheng Shi, Yongcan Huang +2
Harnessing the power of Large Language Models (LLMs), this study explores the use of three state-of-the-art LLMs, specifically GPT-3.5-turbo, LLaMA3-8B, and LLaMA3-70B, for crash s…