9 citations · 33 across the 15 of their papers we have counts for
16 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…
Cross-Paradigm Evaluation of Gaze-Based Semantic Object Identification for Intelligent Vehicles
Penghao Deng, Jidong J. Yang, Jiachen Bian
Understanding where drivers direct their visual attention during driving, as characterized by gaze behavior, is critical for developing next-generation advanced driver-assistance s…
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…
VisitHGNN: Heterogeneous Graph Neural Networks for Modeling Point-of-Interest Visit Patterns
Lin Pang, Jidong J. Yang
Understanding how urban residents travel between neighborhoods and destinations is critical for transportation planning, mobility management, and public health. By mining historica…
Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding
Yunxiang Yang, Ningning Xu, Jidong J. Yang
This paper introduces a multi-agent framework for comprehensive highway scene understanding, designed around a mixture-of-experts strategy. In this framework, a large generic visio…
Structured Prompting and Multi-Agent Knowledge Distillation for Traffic Video Interpretation and Risk Inference
Yunxiang Yang, Ningning Xu, Jidong J. Yang
Comprehensive highway scene understanding and robust traffic risk inference are vital for advancing Intelligent Transportation Systems (ITS) and autonomous driving. Traditional app…
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 autonomous vehicle safety in rain: a data-centric approach for clear vision
Mark A. Seferian, Jidong J. Yang
Autonomous vehicles face significant challenges in navigating adverse weather, particularly rain, due to the visual impairment of camera-based systems. In this study, we leveraged…
Leveraging Scene Geometry and Depth Information for Robust Image Deraining
Ningning Xu, Jidong J. Yang
Image deraining holds great potential for enhancing the vision of autonomous vehicles in rainy conditions, contributing to safer driving. Previous works have primarily focused on e…
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…
Image-Based Vehicle Classification by Synergizing Features from Supervised and Self-Supervised Learning Paradigms
Shihan Ma, Jidong J. Yang
This paper introduces a novel approach to leverage features learned from both supervised and self-supervised paradigms, to improve image classification tasks, specifically for vehi…
Semi-supervised multiscale dual-encoding method for faulty traffic data detection
Yongcan Huang, Jidong J. Yang
Inspired by the recent success of deep learning in multiscale information encoding, we introduce a variational autoencoder (VAE) based semi-supervised method for detection of fault…
Co-supervised learning paradigm with conditional generative adversarial networks for sample-efficient classification
Hao Zhen, Yucheng Shi, Jidong J. Yang +1
Classification using supervised learning requires annotating a large amount of classes-balanced data for model training and testing. This has practically limited the scope of appli…
Eco-driving Trajectory Planning of a Heterogeneous Platoon in Urban Environments
Hao Zhen, Sahand Mosharafian, Jidong J. Yang +1
Given the increasing popularity and demand for connected and autonomous vehicles (CAVs), Eco-driving and platooning in highways and urban areas to increase the efficiency of the tr…