9 citations · 33 across the 15 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
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…
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…